Simple Linear-Regression_SalaryData.ipynb

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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as smf\n",
    "import statsmodels.formula.api as sm\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ee00851-b392-4ffe-89c9-9c775cefc5b4",
   "metadata": {
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   },
   "source": [
    "# <font color='Green'>Step-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6af5a286-eddf-4383-9d52-8f8cbfef1f03",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
       "      <td>57081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111.0</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.9</td>\n",
       "      <td>67938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>66029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "      <td>101302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.7</td>\n",
       "      <td>109431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    YearsExperience    Salary\n",
       "0               1.1   39343.0\n",
       "1               1.3   46205.0\n",
       "2               1.5   37731.0\n",
       "3               2.0   43525.0\n",
       "4               2.2   39891.0\n",
       "5               2.9   56642.0\n",
       "6               3.0   60150.0\n",
       "7               3.2   54445.0\n",
       "8               3.2   64445.0\n",
       "9               3.7   57189.0\n",
       "10              3.9   63218.0\n",
       "11              4.0   55794.0\n",
       "12              4.0   56957.0\n",
       "13              4.1   57081.0\n",
       "14              4.5   61111.0\n",
       "15              4.9   67938.0\n",
       "16              5.1   66029.0\n",
       "17              5.3   83088.0\n",
       "18              5.9   81363.0\n",
       "19              6.0   93940.0\n",
       "20              6.8   91738.0\n",
       "21              7.1   98273.0\n",
       "22              7.9  101302.0\n",
       "23              8.2  113812.0\n",
       "24              8.7  109431.0\n",
       "25              9.0  105582.0\n",
       "26              9.5  116969.0\n",
       "27              9.6  112635.0\n",
       "28             10.3  122391.0\n",
       "29             10.5  121872.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing Data\n",
    "\n",
    "df = pd.read_csv('Salary_Data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2f5444d-b1df-41c6-a17f-4e016f473b76",
   "metadata": {
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   "source": [
    "# <font color='Green'>Step-2\n",
    "\n",
    "**Performing EDA on Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2c6ad663-f2dc-413d-bca0-a2a77c48ebad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30 entries, 0 to 29\n",
      "Data columns (total 2 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   YearsExperience  30 non-null     float64\n",
      " 1   Salary           30 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 612.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5e44fa0d-2a7d-465e-8312-ee917febd336",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.313333</td>\n",
       "      <td>76003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.837888</td>\n",
       "      <td>27414.429785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>37731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.200000</td>\n",
       "      <td>56720.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>65237.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>100544.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.500000</td>\n",
       "      <td>122391.000000</td>\n",
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      "text/plain": [
       "       YearsExperience         Salary\n",
       "count        30.000000      30.000000\n",
       "mean          5.313333   76003.000000\n",
       "std           2.837888   27414.429785\n",
       "min           1.100000   37731.000000\n",
       "25%           3.200000   56720.750000\n",
       "50%           4.700000   65237.000000\n",
       "75%           7.700000  100544.750000\n",
       "max          10.500000  122391.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c1540198-df7e-46a4-bf8e-c24576f1d724",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    0\n",
       "Salary             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "284a2b18-11a4-46bb-9539-5b3b8dc29d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7692b8c8-e4f1-4d23-8348-97bbb186b9f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [YearsExperience, Salary]\n",
       "Index: []"
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     "execution_count": 8,
     "metadata": {},
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   "source": [
    "df[df.duplicated()]"
   ]
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   "source": [
    "# <font color='Green'>Step-3\n",
    "\n",
    "**Plotting the data to check for outliers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6af1396b-ab2e-47c6-a5e2-c30db190d96f",
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   "outputs": [
    {
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HjhyJffv2YenSpWjUqBFefPHFCi/jSbVv3x67d+9GWloaBg4ciLt37wIAtFot1Go15s2bZ/JzWJGSLm8kAv4qdnv37o0PP/xQ+rLwsIf/diwtLYvts1u2bCl23kRZ/vzzT5PXDRs2RMuWLZGfn19mv5deeglHjhwx2Xfz8vKwZs0auLu7Sz8rV5X09HRMmjQJr7zyCqZNm4b3338fO3bswGeffVYs9vfff8e2bduk19nZ2fjss8/QsWNH6QjNkCFDkJKSgr179xbrn5WVhQcPHlQ4x4q8l+XVr18/2NraIi4uDvfu3TOZV/S34OvrixYtWuD9999Hbm6uWcatLXjEhUy8/fbbuHPnDl5++WV4enqioKAAhw4dwubNm+Hu7o6wsDAAf+041tbWGDhwIMaNG4fc3FysXbsWTk5OJe6cDxswYAC+/vprvPzyywgKCkJ6ejoSEhLg5eVV4g5WlhEjRmDy5MnYtm0b3nzzzRq7W+Vzzz2Hb775Bi+99BIGDRqE7du3Q61WY/Xq1Rg5ciQ6d+6MYcOG4ZlnnsHVq1exa9cudO/eHStWrKiRfKn2W7lyJXr06AEfHx+MGTMGzZs3R2ZmJlJSUvDbb79J92kZMGAAZs2ahbCwMHTr1g0nT57Ehg0bpG/65eHl5YXevXvD19cXjo6OOHbsGLZu3frYZ3FNnToVX375JV588UX861//gqOjI9avX4/09HT85z//eaIb250/fx5ffPFFsXaNRoO+fftCCIHXX38dNjY20v2jxo0bh//85z8YP368dAJ/kdatWyM8PBxHjx6FRqPBJ598gszMTJMvW5MmTcKOHTswYMAAvPbaa/D19UVeXh5OnjyJrVu34vLlyxU6ilWkvO9leanVaixZsgSjR49Gly5dMGLECDg4OODnn3/GnTt3sH79elhYWOCjjz7Ciy++iHbt2iEsLAxNmjTBtWvXsH//fqjVavz3v/+t8LrUCjV0NRPVUnv27BGvv/668PT0FA0bNhTW1taiZcuW4u233xaZmZkmsTt27BDt27cXKpVKuLu7iwULFohPPvlEADC5R8mjl0MbjUYxb9480axZM6FUKkWnTp3Ezp07RWhoqGjWrJkUV3RZ4cKFC8vM+aWXXhIAxKFDh8q9nijj0syiSxIreh8XIYT45ptvhJWVlRg6dKh0Kej+/fuFVqsVdnZ2QqVSiRYtWojXXntNHDt2rNz5Ut3Xq1cv0a5dO5O2X3/9VYwaNUo4OzuLevXqiSZNmogBAwaIrVu3SjH37t0T//73v4WLi4uwsbER3bt3FykpKcX2u6LLobds2VJs7Dlz5oiuXbsKe3t7YWNjIzw9PcXcuXNLvA3Co3799VcxaNAgYW9vL1QqlejatavYuXNnsbiy9rmSYkubitZp2bJlxS5xFkKIq1evCrVaLV566SWprVmzZiIoKEjs3btXtG/fXiiVSuHp6VnitsjJyRHR0dGiZcuWwtraWjRu3Fh069ZNvP/++9L2KOuzqaTLoYu20+Pey5I+e4T433u3f/9+k/YdO3aIbt26CRsbG6FWq0XXrl1NLhcXQogTJ06If/7zn6JRo0ZCqVSKZs2aiSFDhojk5OSSN74MKISowNk+RLXQyy+/jJMnT+LixYs1nQoR1ULu7u7w9vbGzp07azoVMgOe40Ky9scff2DXrl0YOXJkTadCRETVgOe4kCylp6fjxx9/xEcffYR69eph3LhxNZ0SERFVAx5xIVn67rvvMHLkSKSnp2P9+vWVvm8DERHJi2wKl4MHD2LgwIFwdXWFQqHA9u3bH9vnwIED6Ny5s/SEz0ef1kny9dprr0EIgStXrmDQoEE1nQ4R1WKXL1/m+S11iGwKl7y8PHTo0AErV64sV3x6ejqCgoLwwgsvIDU1FRMmTMDo0aNLvD6fiIiI5EGWVxUpFAps27YNwcHBpcZMmTIFu3btwqlTp6S2YcOGISsrC4mJidWQJREREZlbnT05NyUlpdhtjrVaLSZMmFBqn/z8fJM7RT548ABpaWlwc3N7ohspEdFfd/TMzc2Fi4tLrd6fjEYj/vjjDzRs2LDYQ/mIqPyMRiMyMzPRqVMnsz6brc4WLnq9HhqNxqRNo9EgOzsbd+/eLfHhWHFxcXjvvfeqK0UiIqI678iRI+jSpYvZlldnC5fKiI6ORlRUlPQ6IyMD3t7eyMjIgFqtrsHMiOQvOzsbbm5uuHr1arU+wbuiDAYDmjZtyv2e6AkV7fOPHkR4UnW2cHF2dkZmZqZJW2ZmJtRqdamPIlcqlVAqldLrog9XtVrNDzAiM7Gzs5PF/sT9nsg8zP3TcO39ofkJBQQEIDk52aQtKSkJAQEBNZQRERERPSnZFC65ublITU1FamoqgL8ud05NTcXVq1cB/PUzz6hRo6T4N954A5cuXcLkyZNx9uxZrFq1Cl999RUmTpxYE+kTERGRGcimcDl27Bg6deqETp06AQCioqLQqVMnxMTEAPjrmTVFRQwAeHh4YNeuXUhKSkKHDh2waNEifPTRR9BqtTWSPxERET052Zzj0rt3b5R1y5mS7orbu3dvnDhxogqzIiIiouokmyMuRERERCxciIiISDZYuBAREZFssHAhIiIi2WDhQkRERLLBwoWIiIhkQzaXQ9PT686dOzh79myp8+/evYvLly/D3d291Mc5AICnpyfq169fFSkSkZlxv6fSsHChWu/s2bPw9fV94uXodDp07tzZDBkRUVXjfk+lYeFCtZ6npyd0Ol2p89PS0vDqq6/iiy++QNu2bctcDhHJA/d7Kg0LF6r16tevX65vTG3btuU3K6I6gvs9lYYn5xIREZFsVLhwOXjwIAYOHAhXV1coFAps375dmnf//n1MmTIFPj4+aNCgAVxdXTFq1Cj8/vvvJsu4desWQkJCoFarYW9vj/DwcOTm5prE/PLLL+jZsydUKhXc3NwQHx9fLJctW7bA09MTKpUKPj4+2L17t8l8IQRiYmLg4uICGxsbBAYG4sKFCxVdZSIiIqolKly45OXloUOHDli5cmWxeXfu3MHx48cxY8YMHD9+HF9//TXOnTuHv//97yZxISEhOH36NJKSkrBz504cPHgQY8eOleZnZ2ejX79+aNasGXQ6HRYuXIiZM2dizZo1UsyhQ4cwfPhwhIeH48SJEwgODkZwcDBOnTolxcTHx+ODDz5AQkICDh8+jAYNGkCr1eLevXsVXW0iIiKqDcQTACC2bdtWZsyRI0cEAHHlyhUhhBBnzpwRAMTRo0elmD179giFQiGuXbsmhBBi1apVwsHBQeTn50sxU6ZMEW3atJFeDxkyRAQFBZmM5e/vL8aNGyeEEMJoNApnZ2excOFCaX5WVpZQKpXiyy+/LNf6ZWRkCADCYDCUK55qhk6nEwCETqer6VSoDAaDQRb7k1zyfNpxv6/9ivaljIwMsy63ys9xMRgMUCgUsLe3BwCkpKTA3t4efn5+UkxgYCAsLCxw+PBhKeb555+HtbW1FKPVanHu3Dncvn1bigkMDDQZS6vVIiUlBQCQnp4OvV5vEmNnZwd/f38p5lH5+fnIzs6WppycnCffAERERGQ2VVq43Lt3D1OmTMHw4cOhVqsBAHq9Hk5OTiZxVlZWcHR0hF6vl2I0Go1JTNHrx8U8PP/hfiXFPCouLg52dnbS5OXlVeF1JiIioqpTZYXL/fv3MWTIEAghsHr16qoaxqyio6NhMBik6cyZMzWdEhERET2kSgqXoqLlypUrSEpKko62AICzszOuX79uEv/gwQPcunULzs7OUkxmZqZJTNHrx8U8PP/hfiXFPEqpVEKtVkuTra1thdabiIiIqpbZC5eiouXChQv4v//7PzRq1MhkfkBAALKyskzuiPjtt9/CaDTC399fijl48CDu378vxSQlJaFNmzZwcHCQYpKTk02WnZSUhICAAACAh4cHnJ2dTWKys7Nx+PBhKYaI5KWs2zEAvAUC0dOgwoVLbm4uUlNTkZqaCuCvk2BTU1Nx9epV3L9/H4MGDcKxY8ewYcMGFBYWQq/XQ6/Xo6CgAMBfdzns378/xowZgyNHjuDHH39EZGQkhg0bBldXVwDAiBEjYG1tjfDwcJw+fRqbN2/GsmXLEBUVJeUxfvx4JCYmYtGiRTh79ixmzpyJY8eOITIyEgCgUCgwYcIEzJkzBzt27MDJkycxatQouLq6Ijg4+Ak3GxHVhLJuxwDwFghET4WKXoa0f/9+AaDYFBoaKtLT00ucB0Ds379fWsaff/4phg8fLho2bCjUarUICwsTOTk5JuP8/PPPokePHkKpVIomTZqI+fPnF8vlq6++Eq1btxbW1taiXbt2YteuXSbzjUajmDFjhtBoNEKpVIo+ffqIc+fOlXtdeTm0PPCySHkw92XGeOR2DOa4BUJV5ElVg/t97VdVl0NX+FlFvXv3hhCirELosctwdHTExo0by4xp3749vv/++zJjBg8ejMGDB5c6X6FQYNasWZg1a9ZjcyIieXvcLRCGDRtWYr/8/Hzk5+dLr7Ozs6s8VyKqPD6riIjqhMrcAgEofhsENze3Ks2TiJ4MCxcieqo9ehuEjIyMmk6JiMrAwoWI6oTK3AIBKH4bhIdv30BEtQ8LFyKqE3gLBKKnQ4VPziUiqim5ubm4ePGi9LrodgyOjo5o2rSpdAuEVq1awcPDAzNmzOAtEIjqGBYuRCQbx44dwwsvvCC9Lrq3U2hoKNatW4fJkycjLy8PY8eORVZWFnr06IHExESoVKqaSpmIzIyFCxHJxuNux8BbIBDVfTzHhYiIiGSDhQsRERHJBgsXIiIikg0WLkRERCQbLFyIiIhINli4EBERkWywcCEiIiLZYOFCREREssHChYiIiGSDhQsRERHJBgsXIiIikg0WLkRERCQbLFyIiIhINli4EBERkWywcCEiIiLZYOFCREREssHChYiIiGSDhQsRERHJBgsXIiIikg0WLkRERCQbLFyIiIhINmRXuKxcuRLu7u5QqVTw9/fHkSNHyoxfunQp2rRpAxsbG7i5uWHixIm4d+9eNWVLRERE5iSrwmXz5s2IiopCbGwsjh8/jg4dOkCr1eL69eslxm/cuBFTp05FbGws0tLS8PHHH2Pz5s2YNm1aNWdORERE5iCrwmXx4sUYM2YMwsLC4OXlhYSEBNSvXx+ffPJJifGHDh1C9+7dMWLECLi7u6Nfv34YPnz4Y4/SEBERUe0km8KloKAAOp0OgYGBUpuFhQUCAwORkpJSYp9u3bpBp9NJhcqlS5ewe/duvPTSS9WSMxEREZmXVU0nUF43b95EYWEhNBqNSbtGo8HZs2dL7DNixAjcvHkTPXr0gBACDx48wBtvvFHqT0X5+fnIz8+XXufk5JhvBYiIiOiJyeaIS2UcOHAA8+bNw6pVq3D8+HF8/fXX2LVrF2bPnl1ifFxcHOzs7KTJy8urmjMmIiKissimcGncuDEsLS2RmZlp0p6ZmQlnZ+cS+8yYMQMjR47E6NGj4ePjg5dffhnz5s1DXFwcjEZjsfjo6GgYDAZpOnPmTJWsCxEREVWObAoXa2tr+Pr6Ijk5WWozGo1ITk5GQEBAiX3u3LkDCwvTVbS0tAQACCGKxSuVSqjVammytbU14xoQERHRk5LNOS4AEBUVhdDQUPj5+aFr165YunQp8vLyEBYWBgAYNWoUmjRpgri4OADAwIEDsXjxYnTq1An+/v64ePEiZsyYgYEDB0oFDBEREcmHrAqXoUOH4saNG4iJiYFer0fHjh2RmJgonbB79epVkyMs06dPh0KhwPTp03Ht2jU888wzGDhwIObOnVtTq0BERERPQFaFCwBERkYiMjKyxHkHDhwweW1lZYXY2FjExsZWQ2ZERERU1WRzjgsRERERCxciIiKSDRYuREREJBssXIiIiEg2WLgQERGRbLBwISIiItlg4UJERESywcKFiIiIZIOFCxEREckGCxciIiKSDRYuREREJBssXIiIiEg2WLgQERGRbLBwISIiItlg4UJERESywcKFiIiIZKPChcvBgwcxcOBAuLq6QqFQYPv27SbzhRCIiYmBi4sLbGxsEBgYiAsXLpjE3Lp1CyEhIVCr1bC3t0d4eDhyc3NNYn755Rf07NkTKpUKbm5uiI+PL5bLli1b4OnpCZVKBR8fH+zevbvCuRBR3VFYWIgZM2bAw8MDNjY2aNGiBWbPng0hRE2nRkRmUuHCJS8vDx06dMDKlStLnB8fH48PPvgACQkJOHz4MBo0aACtVot79+5JMSEhITh9+jSSkpKwc+dOHDx4EGPHjpXmZ2dno1+/fmjWrBl0Oh0WLlyImTNnYs2aNVLMoUOHMHz4cISHh+PEiRMIDg5GcHAwTp06VaFciKjuWLBgAVavXo0VK1YgLS0NCxYsQHx8PJYvX17TqRGRmSjEE3wVUSgU2LZtG4KDgwH8dYTD1dUV//73v/HOO+8AAAwGAzQaDdatW4dhw4YhLS0NXl5eOHr0KPz8/AAAiYmJeOmll/Dbb7/B1dUVq1evxrvvvgu9Xg9ra2sAwNSpU7F9+3acPXsWADB06FDk5eVh586dUj7PPfccOnbsiISEhHLl8ji//fYb3NzcYDAYoFarK7uZqBwuXLiAnJycSvVNS0vDq6++ii+++AJt27at1DJsbW3RqlWrSvWl8snOzoadnV2V7k8DBgyARqPBxx9/LLW98sorsLGxwRdffFFr8qQnd/z4cfj6+kKn06Fz5841nQ6VoGhfysjIwLPPPmu25VqZbUkA0tPTodfrERgYKLXZ2dnB398fKSkpGDZsGFJSUmBvby8VLQAQGBgICwsLHD58GC+//DJSUlLw/PPPS0ULAGi1WixYsAC3b9+Gg4MDUlJSEBUVZTK+VquVfroqTy6Pys/PR35+vvS6sv+QUsVcuHABrVu3fuLlvPrqq0/U//z58yxeZK5bt25Ys2YNzp8/j9atW+Pnn3/GDz/8gMWLF9d0akRkJmYtXPR6PQBAo9GYtGs0GmmeXq+Hk5OTaRJWVnB0dDSJ8fDwKLaMonkODg7Q6/WPHedxuTwqLi4O7733XvlWlsymqECs7BGTu3fv4vLly3B3d4eNjU2F+xcdsWGhKn9Tp05FdnY2PD09YWlpicLCQsydOxchISGl9nn0C0t2dnZ1pEpElWTWwkXuoqOjTY7iXLt2DV5eXjWY0dOlbdu2lT7k2717dzNnQ3L01VdfYcOGDdi4cSPatWuH1NRUTJgwAa6urggNDS2xD7+wEMmLWS+HdnZ2BgBkZmaatGdmZkrznJ2dcf36dZP5Dx48wK1bt0xiSlrGw2OUFvPw/Mfl8iilUgm1Wi1Ntra25VhrIqotJk2ahKlTp2LYsGHw8fHByJEjMXHiRMTFxZXaJzo6GgaDQZoyMjKqMWMiqiizFi4eHh5wdnZGcnKy1JadnY3Dhw8jICAAABAQEICsrCzodDop5ttvv4XRaIS/v78Uc/DgQdy/f1+KSUpKQps2beDg4CDFPDxOUUzROOXJhYjqljt37sDCwvRjzdLSEkajsdQ+j35h4Qm5RLVbhX8qys3NxcWLF6XX6enpSE1NhaOjI5o2bYoJEyZgzpw5aNWqFTw8PDBjxgy4urpKVx61bdsW/fv3x5gxY5CQkID79+8jMjISw4YNg6urKwBgxIgReO+99xAeHo4pU6bg1KlTWLZsGZYsWSKNO378ePTq1QuLFi1CUFAQNm3ahGPHjkmXTCsUisfmQkR1y8CBAzF37lw0bdoU7dq1w4kTJ7B48WK8/vrrNZ0aEZmLqKD9+/cLAMWm0NBQIYQQRqNRzJgxQ2g0GqFUKkWfPn3EuXPnTJbx559/iuHDh4uGDRsKtVotwsLCRE5OjknMzz//LHr06CGUSqVo0qSJmD9/frFcvvrqK9G6dWthbW0t2rVrJ3bt2mUyvzy5lCUjI0MAEAaDodx9qOJ0Op0AIHQ63VM5/tPCYDBU+f6UnZ0txo8fL5o2bSpUKpVo3ry5ePfdd0V+fn6typOeHPfb2q9oX8rIyDDrcp/oPi51He/jUj1q+n4MNT3+00Iu90eRS55PO+63tV9V3ceFzyoiIiIi2WDhQkRERLLB+7gQEVGNeNJHfTz838rgoz7kiYULERFVOz7qgyqLhQsREVU7PuqDKouFCxER1Rg+6oMqiifnEhERkWywcCEiIiLZYOFCREREssHChYiIiGSDhQsRERHJBgsXIiIikg0WLkRERCQbLFyIiIhINli4EBERkWywcCEiIiLZYOFCREREssHChYiIiGSDhQsRERHJBgsXIiIikg0WLkRERCQbLFyIiIhINli4EBERkWywcCEiIiLZkF3hsnLlSri7u0OlUsHf3x9HjhwpMz4rKwsRERFwcXGBUqlE69atsXv37mrKloiIiMzJqqYTqIjNmzcjKioKCQkJ8Pf3x9KlS6HVanHu3Dk4OTkViy8oKEDfvn3h5OSErVu3okmTJrhy5Qrs7e2rP3kiIiJ6YrIqXBYvXowxY8YgLCwMAJCQkIBdu3bhk08+wdSpU4vFf/LJJ7h16xYOHTqEevXqAQDc3d2rM2UiIiIyI9n8VFRQUACdTofAwECpzcLCAoGBgUhJSSmxz44dOxAQEICIiAhoNBp4e3tj3rx5KCwsLDE+Pz8f2dnZ0pSTk1Ml60JERESVI5vC5ebNmygsLIRGozFp12g00Ov1Jfa5dOkStm7disLCQuzevRszZszAokWLMGfOnBLj4+LiYGdnJ01eXl5mXw8iIiKqPNkULpVhNBrh5OSENWvWwNfXF0OHDsW7776LhISEEuOjo6NhMBik6cyZM9WcMREREZVFNue4NG7cGJaWlsjMzDRpz8zMhLOzc4l9XFxcUK9ePVhaWkptbdu2hV6vR0FBAaytrU3ilUollEql9Do7O9uMa0BERERPSjZHXKytreHr64vk5GSpzWg0Ijk5GQEBASX26d69Oy5evAij0Si1nT9/Hi4uLsWKFiIiIqr9ZFO4AEBUVBTWrl2L9evXIy0tDW+++Sby8vKkq4xGjRqF6OhoKf7NN9/ErVu3MH78eJw/fx67du3CvHnzEBERUVOrQERERE9ANj8VAcDQoUNx48YNxMTEQK/Xo2PHjkhMTJRO2L169SosLP5Xi7m5uWHv3r2YOHEi2rdvjyZNmmD8+PGYMmVKTa0CERERPQFZFS4AEBkZicjIyBLnHThwoFhbQEAAfvrppyrOioiIiKqDrH4qIiIioqcbCxciIiKSDRYuREREJBssXIiIiEg2WLgQERGRbLBwISIiItlg4UJERESywcKFiIiIZIOFCxEREckGCxciIiKSDRYuREREJBssXIiIiEg2WLgQERGRbLBwISIiItlg4UJEdcq1a9fw6quvolGjRrCxsYGPjw+OHTtW02kRkZlY1XQCRETmcvv2bXTv3h0vvPAC9uzZg2eeeQYXLlyAg4NDTadGRGbCwoWI6owFCxbAzc0Nn376qdTm4eFRgxkRkbnxpyIiqjN27NgBPz8/DB48GE5OTujUqRPWrl1bZp/8/HxkZ2ebTERUe7FwIaI649KlS1i9ejVatWqFvXv34s0338S//vUvrF+/vtQ+cXFxsLOzkyY3N7dqzJiIKoqFCxHVGUajEZ07d8a8efPQqVMnjB07FmPGjEFCQkKpfaKjo2EwGKQpIyOjGjMmoooye+FSWFiIGTNmwMPDAzY2NmjRogVmz54NIYQUI4RATEwMXFxcYGNjg8DAQFy4cMFkObdu3UJISAjUajXs7e0RHh6O3Nxck5hffvkFPXv2hEqlgpubG+Lj44vls2XLFnh6ekKlUsHHxwe7d+829yoTUS3h4uICLy8vk7a2bdvi6tWrpfZRKpVQq9UmExHVXmYvXBYsWIDVq1djxYoVSEtLw4IFCxAfH4/ly5dLMfHx8fjggw+QkJCAw4cPo0GDBtBqtbh3754UExISgtOnTyMpKQk7d+7EwYMHMXbsWGl+dnY2+vXrh2bNmkGn02HhwoWYOXMm1qxZI8UcOnQIw4cPR3h4OE6cOIHg4GAEBwfj1KlT5l5tIqoFunfvjnPnzpm0nT9/Hs2aNauhjIjI7ISZBQUFiddff92k7Z///KcICQkRQghhNBqFs7OzWLhwoTQ/KytLKJVK8eWXXwohhDhz5owAII4ePSrF7NmzRygUCnHt2jUhhBCrVq0SDg4OIj8/X4qZMmWKaNOmjfR6yJAhIigoyCQXf39/MW7cuHKtS0ZGhgAgDAZDueKpcnQ6nQAgdDrdUzn+08JgMFT5/nTkyBFhZWUl5s6dKy5cuCA2bNgg6tevL7744otalSfV/H5X0+M/DYr2pYyMDLMu1+xHXLp164bk5GScP38eAPDzzz/jhx9+wIsvvggASE9Ph16vR2BgoNTHzs4O/v7+SElJAQCkpKTA3t4efn5+UkxgYCAsLCxw+PBhKeb555+HtbW1FKPVanHu3Dncvn1binl4nKKYonEe9ejVBTk5OU+6OYioGnXp0gXbtm3Dl19+CW9vb8yePRtLly5FSEhITadGRGZi9vu4TJ06FdnZ2fD09ISlpSUKCwsxd+5c6YNDr9cDADQajUk/jUYjzdPr9XBycjJN1MoKjo6OJjGP3p+haJl6vR4ODg7Q6/VljvOouLg4vPfee5VZbSKqJQYMGIABAwbUdBpEVEXMfsTlq6++woYNG7Bx40YcP34c69evx/vvv1/m5Yi1xaNXF5w5c6amUyIiIqKHmP2Iy6RJkzB16lQMGzYMAODj44MrV64gLi4OoaGhcHZ2BgBkZmbCxcVF6peZmYmOHTsCAJydnXH9+nWT5T548AC3bt2S+js7OyMzM9Mkpuj142KK5j9KqVRCqVRKr3kjKiIiotrF7Edc7ty5AwsL08VaWlrCaDQC+Ov2287OzkhOTpbmZ2dn4/DhwwgICAAABAQEICsrCzqdTor59ttvYTQa4e/vL8UcPHgQ9+/fl2KSkpLQpk0b6bkkAQEBJuMUxRSNQ0RERPJi9sJl4MCBmDt3Lnbt2oXLly9j27ZtWLx4MV5++WUAgEKhwIQJEzBnzhzs2LEDJ0+exKhRo+Dq6org4GAAf913oX///hgzZgyOHDmCH3/8EZGRkRg2bBhcXV0BACNGjIC1tTXCw8Nx+vRpbN68GcuWLUNUVJSUy/jx45GYmIhFixbh7NmzmDlzJo4dO4bIyEhzrzYRERFVA7P/VLR8+XLMmDEDb731Fq5fvw5XV1eMGzcOMTExUszkyZORl5eHsWPHIisrCz169EBiYiJUKpUUs2HDBkRGRqJPnz6wsLDAK6+8gg8++ECab2dnh3379iEiIgK+vr5o3LgxYmJiTO710q1bN2zcuBHTp0/HtGnT0KpVK2zfvh3e3t7mXm0iIiKqBmYvXGxtbbF06VIsXbq01BiFQoFZs2Zh1qxZpcY4Ojpi48aNZY7Vvn17fP/992XGDB48GIMHDy4zhoiIiOSBzyoiIiIi2WDhQkRERLLBwoWIiIhkg4ULERERyQYLFyIiIpINFi5EREQkGyxciIiISDZYuBAREZFssHAhIiIi2WDhQkRERLLBwoWIiIhkg4ULERERyQYLFyIiIpINFi5EREQkGyxciIiISDZYuBAREZFssHAhIiIi2WDhQkRERLLBwoWIiIhkg4ULERERyYbsCpeVK1fC3d0dKpUK/v7+OHLkSLn6bdq0CQqFAsHBwVWbIBEREVUZWRUumzdvRlRUFGJjY3H8+HF06NABWq0W169fL7Pf5cuX8c4776Bnz57VlCkRERFVBVkVLosXL8aYMWMQFhYGLy8vJCQkoH79+vjkk09K7VNYWIiQkBC89957aN68eTVmS0REROYmm8KloKAAOp0OgYGBUpuFhQUCAwORkpJSar9Zs2bByckJ4eHhjx0jPz8f2dnZ0pSTk2OW3ImIiMg8ZFO43Lx5E4WFhdBoNCbtGo0Ger2+xD4//PADPv74Y6xdu7ZcY8TFxcHOzk6avLy8njhvIiIiMh+rmk6gquTk5GDkyJFYu3YtGjduXK4+0dHRiIqKkl5fu3aNxQsRURVxbqiATdZ54Pfq/w5tk3Uezg0V1T4uPTnZFC6NGzeGpaUlMjMzTdozMzPh7OxcLP7XX3/F5cuXMXDgQKnNaDQCAKysrHDu3Dm0aNHCpI9SqYRSqZReZ2dnm3MViIjoIeN8rdH24DjgYPWP3fb/j0/yI5vCxdraGr6+vkhOTpYuaTYajUhOTkZkZGSxeE9PT5w8edKkbfr06cjJycGyZcvg5uZWHWkTEVEpPtQVYGjMOrT19Kz2sdPOnsWHi0bg79U+Mj0p2RQuABAVFYXQ0FD4+fmha9euWLp0KfLy8hAWFgYAGDVqFJo0aYK4uDioVCp4e3ub9Le3tweAYu1ERFT99LkCd+1bA64dq33su3oj9Lmi2selJyerwmXo0KG4ceMGYmJioNfr0bFjRyQmJkon7F69ehUWFrI535iIiIgqSFaFCwBERkaW+NMQABw4cKDMvuvWrTN/QkRERFRteHiCiIiIZIOFCxEREckGCxciIiKSDRYuREREJBssXIiIiEg2WLgQERGRbLBwISIiItlg4UJERESywcKFiIiIZIOFCxEREcmG7G75T3WTc0MFbLLOA79Xfy1tk3Uezg0V1T4uERFVHAsXqhXG+Vqj7cFxwMHqH7vt/x+f6p758+cjOjoa48ePx9KlS2s6HSIyAxYuVCt8qCvA0Jh1aOvpWe1jp509iw8XjcDfq31kqkpHjx7Fhx9+iPbt29d0KkRkRixcqFbQ5wrctW8NuHas9rHv6o3Q54pqH5eqTm5uLkJCQrB27VrMmTOnptMhIjPiyblEVOdEREQgKCgIgYGBNZ0KEZkZj7gQUZ2yadMmHD9+HEePHi1XfH5+PvLz86XX2dnZVZUaEZkBj7gQUZ2RkZGB8ePHY8OGDVCpVOXqExcXBzs7O2lyc3Or4iyJ6EmwcCGiOkOn0+H69evo3LkzrKysYGVlhe+++w4ffPABrKysUFhYWKxPdHQ0DAaDNGVkZNRA5kRUXvypiIjqjD59+uDkyZMmbWFhYfD09MSUKVNgaWlZrI9SqYRSqayuFInoCbFwIaI6w9bWFt7e3iZtDRo0QKNGjYq1E5E88aciIiIikg0ecSGiOu3AgQM1nQIRmRGPuBAREZFssHAhIiIi2aiSwuXatWt49dVX0ahRI9jY2MDHxwfHjh2T5gshEBMTAxcXF9jY2CAwMBAXLlwwWcatW7cQEhICtVoNe3t7hIeHIzc31yTml19+Qc+ePaFSqeDm5ob4+PhiuWzZsgWenp5QqVTw8fHB7t27q2KViYiIqBqYvXC5ffs2unfvjnr16mHPnj04c+YMFi1aBAcHBykmPj4eH3zwARISEnD48GE0aNAAWq0W9+7dk2JCQkJw+vRpJCUlYefOnTh48CDGjh0rzc/Ozka/fv3QrFkz6HQ6LFy4EDNnzsSaNWukmEOHDmH48OEIDw/HiRMnEBwcjODgYJw6dcrcq01ERETVQZjZlClTRI8ePUqdbzQahbOzs1i4cKHUlpWVJZRKpfjyyy+FEEKcOXNGABBHjx6VYvbs2SMUCoW4du2aEEKIVatWCQcHB5Gfn28ydps2baTXQ4YMEUFBQSbj+/v7i3HjxpVrXTIyMgQAYTAYyhVPlaPT6QQAodPpnsrxnxYGg0EW+5Nc8pS7mt7vanr8p0HRvpSRkWHW5Zr9iMuOHTvg5+eHwYMHw8nJCZ06dcLatWul+enp6dDr9SYPP7Ozs4O/vz9SUlIAACkpKbC3t4efn58UExgYCAsLCxw+fFiKef7552FtbS3FaLVanDt3Drdv35ZiHn3ImlarlcYhIiIieTF74XLp0iWsXr0arVq1wt69e/Hmm2/iX//6F9avXw8A0Ov1AACNRmPST6PRSPP0ej2cnJxM5ltZWcHR0dEkpqRlPDxGaTFF8x+Vn5+P7OxsacrJyanw+hMREVHVMft9XIxGI/z8/DBv3jwAQKdOnXDq1CkkJCQgNDTU3MOZVVxcHN57772aToOIiIhKYfYjLi4uLvDy8jJpa9u2La5evQoAcHZ2BgBkZmaaxGRmZkrznJ2dcf36dZP5Dx48wK1bt0xiSlrGw2OUFlM0/1GPPmztzJkz5VtpIiIiqhZmL1y6d++Oc+fOmbSdP38ezZo1AwB4eHjA2dkZycnJ0vzs7GwcPnwYAQEBAICAgABkZWVBp9NJMd9++y2MRiP8/f2lmIMHD+L+/ftSTFJSEtq0aSNdwRQQEGAyTlFM0TiPUiqVUKvV0mRra1vZzUBERERVwOyFy8SJE/HTTz9h3rx5uHjxIjZu3Ig1a9YgIiICAKBQKDBhwgTMmTMHO3bswMmTJzFq1Ci4uroiODgYwF9HaPr3748xY8bgyJEj+PHHHxEZGYlhw4bB1dUVADBixAhYW1sjPDwcp0+fxubNm7Fs2TJERUVJuYwfPx6JiYlYtGgRzp49i5kzZ+LYsWOIjIw092oTERFRNTD7OS5dunTBtm3bEB0djVmzZsHDwwNLly5FSEiIFDN58mTk5eVh7NixyMrKQo8ePZCYmAiVSiXFbNiwAZGRkejTpw8sLCzwyiuv4IMPPpDm29nZYd++fYiIiICvry8aN26MmJgYk3u9dOvWDRs3bsT06dMxbdo0tGrVCtu3b+dTYomIiGSqSh6yOGDAAAwYMKDU+QqFArNmzcKsWbNKjXF0dMTGjRvLHKd9+/b4/vvvy4wZPHgwBg8eXHbCREREJAt8VhERERHJBgsXIiIikg0WLkRERCQbVXKOC1FF3LlzBwBw/PjxSvW/e/cuLl++DHd3d9jY2FS4f1paWqXGJSKi6sfChWrc2bNnAQBjxoyp0Tx43x6i6sMvLFRZLFyoxhXdv8fT0xP169evcP+0tDS8+uqr+OKLL9C2bdtK5WBra4tWrVpVqi8RVRy/sFBlsXChGte4cWOMHj36iZfTtm1bdO7c2QwZEVFV4xcWqiwWLkREVO34hYUqi1cVERERkWywcCEiIiLZYOFCREREssHChYiIiGSDhQsRERHJBgsXIiIikg0WLkRERCQbsitcVq5cCXd3d6hUKvj7++PIkSOlxq5duxY9e/aEg4MDHBwcEBgYWGY8ERER1W6yKlw2b96MqKgoxMbG4vjx4+jQoQO0Wi2uX79eYvyBAwcwfPhw7N+/HykpKXBzc0O/fv1w7dq1as6ciIiIzEFWhcvixYsxZswYhIWFwcvLCwkJCahfvz4++eSTEuM3bNiAt956Cx07doSnpyc++ugjGI1GJCcnV3PmREREZA6yKVwKCgqg0+kQGBgotVlYWCAwMBApKSnlWsadO3dw//59ODo6VlWaREREVIVk86yimzdvorCwEBqNxqRdo9FITxl9nClTpsDV1dWk+HlYfn4+8vPzpdc5OTmVT5iIiIjMTjZHXJ7U/PnzsWnTJmzbtg0qlarEmLi4ONjZ2UmTl5dXNWdJREREZZFN4dK4cWNYWloiMzPTpD0zMxPOzs5l9n3//fcxf/587Nu3D+3bty81Ljo6GgaDQZrOnDljltyJiIjIPGRTuFhbW8PX19fkxNqiE20DAgJK7RcfH4/Zs2cjMTERfn5+ZY6hVCqhVqulydbW1mz5ExER0ZOTzTkuABAVFYXQ0FD4+fmha9euWLp0KfLy8hAWFgYAGDVqFJo0aYK4uDgAwIIFCxATE4ONGzfC3d0der0eANCwYUM0bNiwxtaDiIiIKkdWhcvQoUNx48YNxMTEQK/Xo2PHjkhMTJRO2L169SosLP53EGn16tUoKCjAoEGDTJYTGxuLmTNnVmfqREREZAayKlwAIDIyEpGRkSXOO3DggMnry5cvV31CREREVG1kc44LEREREQsXIiIikg0WLkRERCQbLFyIiIhINli4EBERkWywcCEiIiLZYOFCREREssHChYiIiGSDhQsRERHJBgsXIiIikg0WLkRUp8TFxaFLly6wtbWFk5MTgoODce7cuZpOi4jMhIULEdUp3333HSIiIvDTTz8hKSkJ9+/fR79+/ZCXl1fTqRGRGcjuIYtERGVJTEw0eb1u3To4OTlBp9Ph+eefr6GsiMhcWLgQUZ1mMBgAAI6OjiXOz8/PR35+vvQ6Ozu7WvIiosrhT0VEVGcZjUZMmDAB3bt3h7e3d4kxcXFxsLOzkyY3N7dqzpKIKoKFCxHVWRERETh16hQ2bdpUakx0dDQMBoM0ZWRkVGOGRFRR/KmIiOqkyMhI7Ny5EwcPHsSzzz5bapxSqYRSqazGzIjoSbBwIaI6RQiBt99+G9u2bcOBAwfg4eFR0ykRkRmxcCGiOiUiIgIbN27EN998A1tbW+j1egCAnZ0dbGxsajg7InpSPMeFiOqU1atXw2AwoHfv3nBxcZGmzZs313RqRGQGPOJCRHWKEKKmUyCiKsQjLkRERCQbLFyIiIhINli4EBERkWywcCEiIiLZYOFCREREssHChYiIiGSDhQsRERHJBgsXIiIiko0qL1zmz58PhUKBCRMmSG337t1DREQEGjVqhIYNG+KVV15BZmamSb+rV68iKCgI9evXh5OTEyZNmoQHDx6YxBw4cACdO3eGUqlEy5YtsW7dumLjr1y5Eu7u7lCpVPD398eRI0eqYjWJiIioGlRp4XL06FF8+OGHaN++vUn7xIkT8d///hdbtmzBd999h99//x3//Oc/pfmFhYUICgpCQUEBDh06hPXr12PdunWIiYmRYtLT0xEUFIQXXngBqampmDBhAkaPHo29e/dKMZs3b0ZUVBRiY2Nx/PhxdOjQAVqtFtevX6/K1SYiIqKqIqpITk6OaNWqlUhKShK9evUS48ePF0IIkZWVJerVqye2bNkixaalpQkAIiUlRQghxO7du4WFhYXQ6/VSzOrVq4VarRb5+flCCCEmT54s2rVrZzLm0KFDhVarlV537dpVRERESK8LCwuFq6uriIuLK9c6ZGRkCADCYDBUbOWpWul0OgFA6HS6mk6FymAwGGSxP8klz6cd9/var2hfysjIMOtyq+yIS0REBIKCghAYGGjSrtPpcP/+fZN2T09PNG3aFCkpKQCAlJQU+Pj4QKPRSDFarRbZ2dk4ffq0FPPosrVarbSMgoIC6HQ6kxgLCwsEBgZKMURERCQvVfKQxU2bNuH48eM4evRosXl6vR7W1tawt7c3addoNNLj5/V6vUnRUjS/aF5ZMdnZ2bh79y5u376NwsLCEmPOnj1bYt75+fnIz8+XXufk5JRjbYmIiKi6mP2IS0ZGBsaPH48NGzZApVKZe/FVKi4uDnZ2dtLk5eVV0ykRERHRQ8xeuOh0Oly/fh2dO3eGlZUVrKys8N133+GDDz6AlZUVNBoNCgoKkJWVZdIvMzMTzs7OAABnZ+diVxkVvX5cjFqtho2NDRo3bgxLS8sSY4qW8ajo6GgYDAZpOnPmTKW3AxEREZmf2QuXPn364OTJk0hNTZUmPz8/hISESP9fr149JCcnS33OnTuHq1evIiAgAAAQEBCAkydPmlz9k5SUBLVaLR0FCQgIMFlGUUzRMqytreHr62sSYzQakZycLMU8SqlUQq1WS5Otra15NgoRERGZhdnPcbG1tYW3t7dJW4MGDdCoUSOpPTw8HFFRUXB0dIRarcbbb7+NgIAAPPfccwCAfv36wcvLCyNHjkR8fDz0ej2mT5+OiIgIKJVKAMAbb7yBFStWYPLkyXj99dfx7bff4quvvsKuXbukcaOiohAaGgo/Pz907doVS5cuRV5eHsLCwsy92kRERFQNquTk3MdZsmQJLCws8MorryA/Px9arRarVq2S5ltaWmLnzp148803ERAQgAYNGiA0NBSzZs2SYjw8PLBr1y5MnDgRy5Ytw7PPPouPPvoIWq1Wihk6dChu3LiBmJgY6PV6dOzYEYmJicVO2CUiIiJ5UAghRE0nUVv99ttvcHNzg8FggFqtrul0qBTHjx+Hr68vdDodOnfuXNPpUCmys7NhZ2dX6/cnueT5tON+X/sV7UsZGRl49tlnzbbcGjniQlQRd+7cKfUSdgBIS0sz+W9pPD09Ub9+fbPmRkRVg/s9lYaFC9V6Z8+eha+v72PjXn311TLn85sZkXxwv6fSsHChWs/T0xM6na7U+Xfv3sXly5fh7u4OGxubMpdDRPLA/Z5Kw8KFar369es/9htT9+7dqykbIqoO3O+pNFX6dGgiIiIic2LhQkRERLLBwoWIiIhkg4ULERERyQYLFyIiIpINFi5EREQkGyxciIiISDZkV7isXLkS7u7uUKlU8Pf3x5EjR8qM37JlCzw9PaFSqeDj44Pdu3dXU6ZERERkbrIqXDZv3oyoqCjExsbi+PHj6NChA7RaLa5fv15i/KFDhzB8+HCEh4fjxIkTCA4ORnBwME6dOlXNmRMREZE5yOrp0P7+/ujSpQtWrFgBADAajXBzc8Pbb7+NqVOnFosfOnQo8vLysHPnTqntueeeQ8eOHZGQkPDY8fh0aCLzkctTl+WSJ1Ft99Q/HbqgoAA6nQ7R0dFSm4WFBQIDA5GSklJin5SUFERFRZm0abVabN++vcT4/Px85OfnS68NBgOAvzY+ET2Zov2oaL+qrbjfE5lH0T5kNBrNulzZFC43b95EYWEhNBqNSbtGoyn10ed6vb7EeL1eX2J8XFwc3nvvvWLtbm5ulcyaiB7VtGnTmk6hXLjfE5lHRkaGWfd72RQu1SE6OtrkCM2tW7fg4eGBU6dOwc7OrgYze3rk5OTAy8sLZ86cga2tbZX1of+pru0nhEBubi5cXFxgYVF7T68zGo34448/0LBhQygUiioZg3+zT66i25DbvPoZDAZ4e3ujbdu2Zl2ubAqXxo0bw9LSEpmZmSbtmZmZcHZ2LrGPs7NzheKVSiWUSmWxdjc3N/7WXU2KDi02adKk3Nu8Mn3of7j9irO3t6/S5XObP7mKbkNu8+pXtJ2trMxbatTerz2PsLa2hq+vL5KTk6U2o9GI5ORkBAQElNgnICDAJB4AkpKSSo0nIiKi2k02R1wAICoqCqGhofDz80PXrl2xdOlS5OXlISwsDAAwatQoNGnSBHFxcQCA8ePHo1evXli0aBGCgoKwadMmHDt2DGvWrKnJ1SAiIqJKklXhMnToUNy4cQMxMTHQ6/Xo2LEjEhMTpRNwr169avLbebdu3bBx40ZMnz4d06ZNQ6tWrbB9+3Z4e3uXazylUonY2NgSfz6iqlGZbc736clw+1U/bvMnV9FtyG1e/apqm8vqPi5ERET0dJPNOS5ERERELFyIiIhINli4EBERkWywcCEiIiLZeOoLl5UrV8Ld3R0qlQr+/v44cuRImfFbtmyBp6cnVCoVfHx8sHv37mrKtO6oyDZfu3YtevbsCQcHBzg4OCAwMFCKP3jwIAYOHAhXV1coFIpSn0FFFf87L7Jp0yYoFAoEBwdXbYJ1UEW3eVZWFiIiIuDi4gKlUonWrVvz86UEZe33Fd3mS5cuRZs2bWBjYwM3NzdMnDgR9+7dq+I1qDsq8xl84MABdO7cGUqlEi1btsS6desqPO5TXbhs3rwZUVFRiI2NxfHjx9GhQwdotVpcv369xPhDhw5h+PDhCA8Px4kTJxAcHIzg4GCcOnWqmjOXr4pu8wMHDmD48OHYv38/UlJS4Obmhn79+uHatWvIy8tDhw4dsHLlympeC3mp6DYvcvnyZbzzzjvo2bNnNWVad1R0mxcUFKBv3764fPkytm7dinPnzmHt2rVo0qRJNWde+5W231d0m2/cuBFTp05FbGws0tLS8PHHH2Pz5s2YNm1adaxGnVDRz+D09HQEBQXhhRdeQGpqKiZMmIDRo0dj7969FRtYPMW6du0qIiIipNeFhYXC1dVVxMXFlRg/ZMgQERQUZNLm7+8vxo0bV6V51iUV3eaPevDggbC1tRXr1683aQcgtm3bZs5U64zKbPMHDx6Ibt26iY8++kiEhoaKf/zjH9WQad1R0W2+evVq0bx5c1FQUFBdKdYJD+/3Fd3mERER4m9/+5tJW1RUlOjevXuV5VuXleczePLkyaJdu3YmbUOHDhVarbZCYz21R1wKCgqg0+kQGBgotVlYWCAwMBApKSkl9klJSTGJBwCtVltqPJmqzDZ/1J07d3D//n04OjpWVZp1SmW3+axZs+Dk5ITw8PDqSLNOqcw237FjBwICAhAREQGNRgNvb2/MmzcPhYWF1ZW2rFVmm3fr1g06nU76OenSpUvYvXs3XnrppWrJ+Wlkrn9DZXXnXHO6efMmCgsLpbvuFtFoNDh79myJffR6fYnxer2+yvKsSyqzzR81ZcoUuLq6Fvvjp5JVZpv/8MMP+Pjjj5GamloNGdY9ldnmly5dwrfffouQkBDs3r0bFy9exFtvvYX79+8jNja2OtKWtcps8xEjRuDmzZvo0aMHhBB48OAB3njjDf5UVIVK+zc0Ozsbd+/ehY2NTbmW89QecSH5mT9/PjZt2oRt27ZBpVLVdDp1Uk5ODkaOHIm1a9eicePGNZ3OU8NoNMLJyQlr1qyBr68vhg4dinfffRcJCQk1nVqddeDAAcybNw+rVq3C8ePH8fXXX2PXrl2YPXt2TadGj/HUHnFp3LgxLC0tkZmZadKemZkJZ2fnEvs4OztXKJ5MVWabF3n//fcxf/58/N///R/at29flWnWKRXd5r/++isuX76MgQMHSm1GoxHAX4+mP3fuHFq0aFG1SctcZf7OXVxcUK9ePVhaWkptbdu2hV6vR0FBAaytras0Z7mrzDafMWMGRo4cidGjRwMAfHx8kJeXh7Fjx+Ldd981ee4dmUdp/4aq1epyH20BnuIjLtbW1vD19UVycrLUZjQakZycjICAgBL7BAQEmMQDQFJSUqnxZKoy2xwA4uPjMXv2bCQmJsLPz686Uq0zKrrNPT09cfLkSaSmpkrT3//+d+kqADc3t+pMX5Yq83fevXt3XLx4USoSAeD8+fNwcXFh0VIOldnmd+7cKVacFBWOgo/wqxJm+ze0YucN1y2bNm0SSqVSrFu3Tpw5c0aMHTtW2NvbC71eL4QQYuTIkWLq1KlS/I8//iisrKzE+++/L9LS0kRsbKyoV6+eOHnyZE2tguxUdJvPnz9fWFtbi61bt4o//vhDmnJyckROTo44ceKEOHHihAAgFi9eLE6cOCGuXLlSU6tXK1V0mz+KVxVVXEW3+dWrV4Wtra2IjIwU586dEzt37hROTk5izpw5NbUKtVZp+/2KFSsqtM1jY2OFra2t+PLLL8WlS5fEvn37RIsWLcSQIUNqatVk53GfwVOnThUjR46U4i9duiTq168vJk2aJNLS0sTKlSuFpaWlSExMrNC4T3XhIoQQy5cvF02bNhXW1taia9eu4qeffpLm9erVS4SGhprEf/XVV6J169bC2tpatGvXTuzatauaM5a/imzzZs2aCQDFptjYWLF///4S5z36nlHF/84fxsKlciq6zQ8dOiT8/f2FUqkUzZs3F3PnzhUPHjyo5qxrv7L2+4ps8/v374uZM2eKFi1aCJVKJdzc3MRbb70lbt++Xf0rJVOP+wwODQ0VvXr1KtanY8eOwtraWjRv3lx8+umnFR5XIQSPiREREZE8PLXnuBAREZH8sHAhIiIi2WDhQkRERLLBwoWIiIhkg4ULERERyQYLFyIiIpINFi5EREQkGyxciIiISDZYuBAREZFssHAhIiIi2WDhQkRERLLBwoWIiIhk4/8B/YXj4mQ3nSQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = (6,4))\n",
    "plt.subplot(121)\n",
    "plt.boxplot(df['Salary'])\n",
    "plt.title('Salary Hike')\n",
    "plt.subplot(122)\n",
    "plt.boxplot(df['YearsExperience'])\n",
    "plt.title('Years of Experience')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abb62169-1372-4615-8439-9a9c2c2f2955",
   "metadata": {},
   "source": [
    "**As you can see there are no outliers in the data**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d5437cf-fad9-46fa-99ce-712a1276b91d",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# <font color='Green'>Step-4\n",
    "\n",
    "Checking the correlation between variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7c67379c-f339-444f-ad50-d25ba57e1c6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YearsExperience</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>0.978242</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 YearsExperience    Salary\n",
       "YearsExperience         1.000000  0.978242\n",
       "Salary                  0.978242  1.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e53ccbf9-e70a-4a42-a7b8-d5e02f2edabc",
   "metadata": {},
   "source": [
    "Visuvalization of correlation between x and y\n",
    "\n",
    "regplot=Regression Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fb980ea4-4a8a-4add-b60d-9353a51e864d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='YearsExperience', ylabel='Salary'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=df['YearsExperience'],y=df['Salary'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6042b97d-2964-424d-bd67-a2c08d5af58c",
   "metadata": {},
   "source": [
    "**As we can see above,**\n",
    "+ There is good correlation between the two variables\n",
    "+ The score is more than 0.8 which is a good sign."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66497874-db45-4a6c-bcfe-050e9e0941ab",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# <font color='Green'>Step-5\n",
    "\n",
    "**Checking for Homoscedasticity or Hetroscedasticity**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "81ee7c1b-53e2-49df-b5ee-523a117de552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,6), facecolor = 'lightgreen')\n",
    "sns.scatterplot(x = df['YearsExperience'], y = df['Salary'])\n",
    "plt.title('Homoscedasticity', fontweight = 'bold', fontsize = 16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9c335e8-7d83-4e15-9dd2-daf44e0f965d",
   "metadata": {},
   "source": [
    "**As you can see in above graph,**\n",
    "+ It shows as the Salary Increases the Years of Experience increases variation is ocnstant along the way in data\n",
    "+ The data doesn't have any specific pattern in the variation. hence, we can say it's Homoscedasticity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4b6bef40-6ea6-4fa6-9f78-e887f9a91a02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    8.053609e+00\n",
       "Salary             7.515510e+08\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.var()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3a1a435-0dd4-4801-80d5-c229582f402a",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# <font color='Green'>Step-6\n",
    "\n",
    "**Featuring Engineering**\n",
    "\n",
    "Trying different transformation of data to estimate normal distribution and remove any skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6ffcd4a1-9dee-43c7-b9c2-9411c1feda69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['YearsExperience'], bins = 10, kde = True)\n",
    "plt.title('Before Transformation')\n",
    "sns.displot(np.log(df['YearsExperience']), bins = 10, kde = True)\n",
    "plt.title('After Transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0cefe072-a82d-424d-8bba-8edcf1c82de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['Before Transformation','After Transformation']\n",
    "sns.distplot(df['YearsExperience'], bins = 10, kde = True)\n",
    "sns.distplot(np.log(df['YearsExperience']), bins = 10, kde = True)\n",
    "plt.legend(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a336bd31-60d7-450a-abad-2543958e28dd",
   "metadata": {},
   "source": [
    "**As you can see,**\n",
    "+ How log transformation affects the data and it scales the values down.\n",
    "+ Before prediction it is necessary to reverse scaled the values, even for calculating RMSE for the models.(Errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9ba5f727-553b-4386-b9a6-cc5def88038f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smf.qqplot(np.log(df['YearsExperience']), line = 'r')\n",
    "plt.title('No transformation')\n",
    "smf.qqplot(np.sqrt(df['YearsExperience']), line = 'r')\n",
    "plt.title('Log transformation')\n",
    "smf.qqplot(np.sqrt(df['YearsExperience']), line = 'r')\n",
    "plt.title('Square root transformation')\n",
    "smf.qqplot(np.cbrt(df['YearsExperience']), line = 'r')\n",
    "plt.title('Cube root transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cdc43ea3-75fb-4220-b555-414f46fa2982",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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37duHd955BwkJCcjOzsaWLVswbdq0Gy6/Z88ejB49+rr7s7Oz4eHh0aosREREJA8tOhV87dq1bfLNKyoqEBoaikcffRT33HPPbT8vOTm5UbFyc3NrkzxERETU+d12ubnnnnsQGxsLBweHWxaRzZs339Y6J06ciIkTJ95uBCM3Nzc4Ojo2+3lEREQkf7ddbrRaLRQKhfH/pRQWFoaamhoEBwfjb3/7G4YOHXrDZWtqalBTU2P8uqysrCMiEhERkURuu9z8cVdUW+2Wai5PT0+sXr0aAwcORE1NDT777DOMGjUKhw4dQv/+/Zt8TkxMDF577bUOTkpERERSadExN1VVVRBCGE8FT09Px5YtW9CnTx+MGzeuTQP+UVBQEIKCgoxfDxkyBKmpqXj33Xfxn//8p8nnLFmyxHgKO9AwcuPj49NuGYmIiEhaLbqI39SpU7Fu3ToAQElJCSIiIrB8+XJMnToVq1atatOAtxIREYGUlJQbPq7RaODg4NDoRkRERPLVonJz7NgxDB8+HADw7bffwsPDA+np6Vi3bh3ef//9Ng14K0lJSTecvJOIiIjMT4t2S1VWVsLe3h4A8Msvv+Cee+6BUqnE4MGDkZ6eftvr0el0jUZd0tLSkJSUBGdnZ/j6+mLJkiW4fPmycZTovffeQ0BAAPr27Yvq6mp89tln2LVrF3755ZeWvAwiIiKSoRaN3AQGBmLr1q3IzMzE9u3bjcfZ5OXlNWu3z9GjRxEeHo7w8HAADdM7hIeHY+nSpQAaLs6XkfH7jLa1tbV4/vnnERISgpEjR+L48ePYuXMnxo4d25KXQURERDLUopGbpUuX4sEHH8Rzzz2HsWPHIioqCkDDKM61onI7Ro0adcNJOAEgNja20dcvvvgiXnzxxZZEJiIiIjPRonJz3333YdiwYcjOzkZoaKjx/rFjx+Luu+9us3BEREREzdWicgMAHh4e183nFBER0epARERERK3RonJTUVGBN998E3FxccjLy4PBYGj0+MWLF9skHBEREVFztajczJs3D3v37sWsWbPg6elpnJaBiIiISGotKjc///wzfvzxx5vO6UREREQkhRadCu7k5ARnZ+e2zkJERETUai0qN2+88QaWLl2KysrKts5DRERE1Cot2i21fPlypKamwt3dHf7+/rC0tGz0+LFjx9okHBG1nBACdXoBAQGNhUrqOEREHaZF5WbatGltHIOIWqtQV4NTl0uRWVyFyyVVKKuqw7VLZKotlHC0toSvsw26udqhp7sdbNQtvhIEEZFJa9Gn27Jly9o6BxG1gMEgcCKrBAdTC5FRdOPdxLX1BuSV1yCvvAZH04thoVQgxFuLqO5d0NXJpgMTExG1vxb/6VZSUoJvv/0WqampeOGFF+Ds7Ixjx47B3d0d3t7ebZmRiJqwJzkPb/58DudyygEACgDd3ezQ090e3o7W6GKnhsZCCQigrLoehboapBVUIDm3HHnlNUjMLEFiZgmCvRwwvq8HuthppH1BRERtpEXl5sSJE4iOjoZWq8WlS5cwf/58ODs7Y/PmzcjIyDDO4k1Eba+0sg6v/3AG3x3LAgBoLJQYGuiCiABnOFhZNvkcV0sVXO016OXpgAnBHsgqrkL8xUIczyzBqStlOJtdjjG93TCihytUSl63iog6txaVm0WLFmHOnDl4++23YW9vb7x/0qRJePDBB9ssHBE1lphRjKfWH0NOWTUUCmDukAB4OVo16/gZhUIBH2cb+DjbYEQPV2w7nY3zuTrsOJOLc9lluH+gD0dxiKhTa9Gp4EeOHMETTzxx3f3e3t7IyclpdSgiut43RzMx4+PfkFNWjW4utvj2ySgsndKnVQcGe2it8EiUP6YP6AorSyUyi6vw0Z5UpOTp2jA5EVHHalG50Wg0KCsru+7+8+fPw9XVtdWhiOh3Qgi8u+M8Xvz2BGr1Bozr447//mkYBvi1zYU0FQoFwn2d8OcxPeDjZI2qOj1iD6Yh/mJhm6yfiKijtajc3HXXXXj99ddRV1cHoOHDMSMjAy+99BLuvffeNg1IZM6EEPjHj2fx77gLAIA/jQnE6ocHwE7T9qdxO9qoMW94N/T3dYRBAP87fgVxZ3MhhLj1k4mITEiLys3y5cuh0+ng6uqKqqoqjBw5EoGBgbC3t8c//vGPts5IZJaEEHjtf2fw2f40AMCyKX3w/LggKNvxgF9LlRL39u+K6N5uAIC4c3n46WQ2Cw4RdSot+vNPq9Vix44dOHDgAI4fPw6dTof+/fsjOjq6rfMRma3341IQe/ASFArgzXtCMGOQb4d8X4VCgTG93GFlqcIPJ7JxILUQCoUCE4M9oFDwTCoiMn3NLjcGgwGxsbHYvHkzLl26BIVCgYCAAHh4eEAIwQ8/ojbwn/hLeHfneQDA36b07bBi80dDurtArVJic+Jl7E8pgNpCieje7h2eg4iouZq1W0oIgbvuugvz5s3D5cuXERISgr59+yI9PR1z5szB3Xff3V45iczG3vP5WPbf0wCAZ6N74JEh/pJlGejvjDv7eQIAdp3Lw4GUAsmyEBHdrmaN3MTGxmLfvn2Ii4vD6NGjGz22a9cuTJs2DevWrcPs2bPbNCSRuUjN12HhhmMwCOD+gV3xzNgeUkfCkO4uqK034JczufjpZDYcbSzR10srdSwiohtq1sjNV199hZdffvm6YgMAY8aMwV/+8hd8+eWXbRaOyJyUVddh/hdHUV5dj4F+TnhjWrDJ7OYd2dMVEQHOEAC+PpKJzJvMY0VEJLVmlZsTJ05gwoQJN3x84sSJOH78eKtDEZkbIQSWfHcSFwsq4KW1wqqHB0BjoZI6lpFCocCUfl4IcrdHvUFg/aF0lFfXSR2LiKhJzSo3RUVFcHe/8QGF7u7uKC4ubnUoInOz/rd0/HgyGxZKBVY+1B+u9qY3/YFKqcADET5ws9egvLoeXx3OgN7AU8SJyPQ0q9zo9XpYWNz4MB2VSoX6+vpWhyIyJ6evlOKNH84CAP4ysRfCfZ0kTnRjGgsVHor0g8ZCiUuFlfj5VLbUkYiIrtOsA4qFEJgzZw40mqb/qqypqWmTUETmorpOj2c3JqFWb0B0b3c8NixA6ki35GqvwfQBPlh/KB0HUwvR1ckGYT6OUsciIjJqVrl55JFHbrkMz5Qiun3/2p6MC3k6uNhp8PZ9/UzmAOJb6ePlgFFBrtiTnI8tiVlwd9DAU2stdSwiIgDNLDdr165trxxEZue3i4X4/EDD1Apv3RsCZ1u1xImaJ7q3Oy4XV+FCng5fHsrAwtGBsLI0nYOgich8tWhuKSJqnapaPV749jiEAB4Y5IOxnfDKv0qFAjMG+cDJxhJFFbXYmnSZc1ARkUlguSGSwIodycgsqoKX1gqvTO4tdZwWs1FbYMYgXygVwImsUiRllkgdiYiI5Yaoo53IKsHnV2f6/sfdIbC3spQ4Uev4OtsYR56+P34FhTqeWEBE0mK5IepA9XoDXvruJAwCmBrmhdG93KSO1CZG9nSFfxdb1NYb8PXRTF7/hogkxXJD1IHW/5aOs9llcLSxxNI7+0gdp80oFQrcP7ArrCyVyCquQty5XKkjEZEZY7kh6iCFuhqs2HEeALB4XBC62JneVYhbw9FGjbvDuwIA9ibn42K+TuJERGSumnUqOBG13Dvbk1FWXY++Xg6YGeErdZx2EeKtxXk/JySkF2NTQhaeGduDp4fL2IZDGe227gcj5fkzQh2DIzdEHeB4Zgm+PpoJAHjtrr5QKTvHxfpa4s5+nnC2VaO0qg7/O35F6jhEZIZYbojamUEILP3vaQgB3B3ujYH+zlJHalcaCxWmD+gKBYDEzBKculwqdSQiMjMsN0TtLDGjGMczS2CrVmHJxF5Sx+kQfl1sMaKnKwBga9JllFfXSZyIiMwJyw1RO6qq1WPb6YYzh/48tgfcHKwkTtRxxvZ2g6fWCpW1emxJ5NWLiajjsNwQtaPdyXmoqKlHN1dbzB1q+jN+tyULpRLTB/pApVTgXE45jqYXSx2JiMwEyw1ROymurEX8xUIAwKuT+0BtYX4/bh4OVhjXp+HqxT+ezEZRRa3EiYjIHJjfpy1RB9l5Jhd6g0CAiy1GBblKHUcyQwNdjFcv3nQ0EwbuniKidsZyQ9QOskurjJNITgz2gEIh31O/b0WpUGD6gK7QWCiRXlSJ/RcKpI5ERDLHckPUDrafzoEAEOytRVcnG6njSM7JVo07+3kCAHaczUV2aZXEiYhIzlhuiNpYar4O53N1UCpgPN6EgP6+TujtYQ+9QWDT0SzU1OuljkREMsVyQ9SGhBDYfjoHADDI3xkuMps/qjUUCgXu7t8VtmoVcsqqjfNsERG1NZYbojZ06koZsoqroFYpMaaXm9RxTI6dxsI4ueYn+y7icFqRxImISI5YbojaiN4g8MvVUZthPVxgb2UpcSLT1MfLAQN8nSAE8PymJOhq6qWOREQyw3JD1EYSM4pRWFELW7UKwwNdpI5j0ib384S3ozUyi6rw9x/OSB2HiGSG5YaoDdQbDNiVnAcAGNnTFRpLlcSJTJuVpQrL7w+FQgFsPJKJnWdypY5ERDLCckPUBhLSi1FSWQd7jQUiArpIHadTGNytC+YNa5iS4i+bT6BQVyNxIiKSC5Ybolaq0xuwJzkfADAyyNUsp1loqefHBSHI3R4Fulq8vOUkJ9ckojbBT2GiVjp6qQilVXVwsLLAIH9nqeN0KlaWKqyYEQpLlQLbT+di87HLUkciIhmQtNzs27cPU6ZMgZeXFxQKBbZu3XrL5+zZswf9+/eHRqNBYGAgYmNj2z0n0Y3U6Q3Yc75h1GZUkBssVfx7obn6emnxbHRPAMDf/nsal0t49WIiah1JP4krKioQGhqKlStX3tbyaWlpmDx5MkaPHo2kpCQ8++yzmDdvHrZv397OSYmadjitCOXV9XC0tsRAPyep43RaT4zohv6+jiivqcfib47DYODuKSJqOQspv/nEiRMxceLE215+9erVCAgIwPLlywEAvXv3xv79+/Huu+9i/Pjx7RWTqEm19b+P2ozu5QYLjtq0mIVKiRX3h2HS+78i/mIh1hxIw7zh3aSORUSdVKf6NI6Pj0d0dHSj+8aPH4/4+PgbPqempgZlZWWNbkRt4beLhaioqYeTjSX6+3LUprX8XWzxyuTeAIC3tyfjfG65xImIqLPqVOUmJycH7u6NJyJ0d3dHWVkZqqqa3k8fExMDrVZrvPn4+HREVJK5Or0B+1MKAABjerlBpVRInEgeHozwxeggV9TWG7BwwzFU1vLqxUTUfJ2q3LTEkiVLUFpaarxlZmZKHYlk4Gh6MXQ19XC0sUSYD0dt2opCocDb94XCzV6D87k6vLyZp4cTUfN1qnLj4eGB3NzGVzLNzc2Fg4MDrK2tm3yORqOBg4NDoxtRa+gNAr9ePdZmRA9Xjtq0MVd7DT58sD9USgW2Jl3B+kMZUkciok6mU5WbqKgoxMXFNbpvx44diIqKkigRmaOkzGKUVNXBTmOBATxDql1EBDjjLxN6AQBe/99pJGWWSBuIiDoVScuNTqdDUlISkpKSADSc6p2UlISMjIa/1JYsWYLZs2cbl3/yySdx8eJFvPjiizh37hw++ugjfPPNN3juueekiE9myCCE8WrEwwJdeF2bdjRveAAm9PVAnV5gwZfHUFxRK3UkIuokJP1kPnr0KMLDwxEeHg4AWLRoEcLDw7F06VIAQHZ2trHoAEBAQAB+/PFH7NixA6GhoVi+fDk+++wzngZOHebU5VIUVtTC2lKFyABejbg9KRQKvD29HwJcbHG5pArPfJ0EPa9/Q0S3QdLr3IwaNeqmBws2dfXhUaNGITExsR1TETVN/GHUZkj3Lpz5uwM4WFli1cP9MW3lAew7n4+3tp3Dy5N6Sx2LiEwcx9SJbtO5nHLklFVDbaFEVHfO/N1Renk44J37QgEAn+y7iG+O8IxHIro5lhui29AwapMHABgc4AwbtaSDnmZnSqgX/jy2BwDgla0nEZ9aKHEiIjJlLDdEt+FiQQUyi6tgoVRgaKCL1HHM0rNje2ByiCfq9AKP/+cozuXwauNE1DSWG6LbsPvqqM1Af2fYW1lKnMY8KZUKLL8/FIP8nVBeXY85a47gCmcQJ6ImsNwQ3UJGUSUu5ldAqQBG9OCojZSsLFX4dPZABLrZIaesGg9/dgj55TVSxyIiE8NyQ3QL1461CfdxgqONWuI05GijxhePRsDb0RoXCyrw8GeHeA0cImqE5YboJrJLq3AupxwKACN7ukodh67ydrTGl/Mi4WavQXJuOR7+/BCKWHCI6CqWG6KbuHZdm2BvLVzsNRKnoT/yd7HFhvmRcLFT4/SVMsz4OB65ZdVSxyIiE8DzWYluoKC8BqculwIARgVx1MYUBbrZ4+snovDQp4dwIU+H6avj8cWjEQhwsZU6WocSQqC0qg6ZRVXIKq5EUWUtKmrqUVtvwImsUigVClhZqmCtVkFrbQlnWzVs1SooFJz0leSJ5YboBvaez4cA0MvDHp7apmedJ+l1d7XDpiej8NBnh5BRVIm7PzqAT2YNRIRMp8eo0xtwLrscSVklSMoowdnsMmQWVaK8pr5Z67G2VMHbyRpdnawR6GoH3y42sFByMJ/kgeWGqAnFlbVIzCwGAIwKcpM4Dd2Kj7MNvntqCOatO4rjmSV4+LNDeGNaX8wY5Ct1tFbTGwROXi7FvvP52H+hAMezSlBTb2hyWRc7DXycreFip4G9xgJqCyVS8nTQGwSq6/SorNWjpKoOZVV1qKrTIyVPh5Q8HfYk50OtUqKXpz36eTuip4cdiw51aiw3RE349UI+DALo5moLX2cbqePQbXC112Dj/MF47uskbDudg5e+O4mjl4rx+tRgWKs71zxgpVV12HUuF3Fn87A/pQAllXWNHnewskCojyPCfRwR0tUR/l1s0NXJpsnXueFQxnX31ekNyCurweWSKlwqrEBKng66mnqcyCrFiaxS2GksMMjfCREBXaC15nWdqPNhuSH6f8qr63D0UsOozWiO2nQq1moVPnqoP1btTcXyX5KxKSELxzKK8a/poQj3dZI63k3ll9dgx5lcbDudg4MpBaj/wwzo9hoLDA10wYierojs5oyALrZQKlt+vIylSglvJ2t4O1kjIsAZQghkFVfh5OVSHM8qQXl1PXYn52PfhQIM8HPCqJ6uvAwCdSosN0T/z4Grv1h8nKzRzcwOTJUDpVKBBaMDEe7riGc2JiE1vwL3rjqI+cO74U9je8BOYzofe5lFlfjlTC62n8rBkfQiiN/7DHq42WFcX3eMCnJDmI8jLFXtt5tIoVDAx9kGPs42GN/XA2eyy3AwtQDphZU4nFaEhPRiDOneBaOD3GBl2blGwcg8mc5POZEJqKytx29pRQAaRm14NknnNaS7C3Y8NwKv/e8MtiRexsf7LmJz4mW8OD4Id4d7w6Idy8KNCCGQnFuO7adysf10Ds5kN54fK7SrFuODPTC+rwe6u9p1eD4AUCkVCPHWIsRbi4sFOuw6m4eLBRX49UIBjmWUYFKwB8J8HPmzQSaN5YboD+JTC1Fbb4Cn1gpBHvZSx6FWcrRR490ZYbiznyde/+EM0gsr8cK3J7BydwqeHh2IqWFe0Fi070hEvd6A41kl+OV0wy6n9MJK42NKBRAR4IzxfT0wrq8HvB1N66y8bi52CBhmi+Tccvx0MhsFulpsSsjCiaxSTA3z4q4qMlksN0RX6WrqcTC1EEDD1Yj5l6l8jO3tjmE9XLD2wCWs3puKS4WVePHbE4j56Szu7d8V08K90dfLoU3+zYUQSCuowP6UAuy/UID4i4Uor/79NG21hRLDA10wvq8HxvZ2Qxc70744pEKhQC8PBwS62WH/hQLEnctDcm453t91AfeEd0Wwt1bqiETXYbkhuurL39JRVaeHi52aH9gypLFQ4cmR3TFrsB++PJSONfsvIaesGp/tT8Nn+9PgpbXCyCBX9Pd1QpiPI3y72NxyVKdOb8Dl4iqczS7DqSulOHW5DKevlKJA13gqCAcrC4wKcsP4vh4YGeRqUsf93C4LpRKjgtzQx9MB3x7LQlZxFTYczkBEgDPuDPGUZDcf0Y10vp8wonZQXafHp7+mAWgYtVFy1Ea2bDUWeHxEdzw2rBv2ns/DN0eysPd8Pq6UVuOrw5n46nAmgIZdRp5aa3SxU0NrbWk8oLe6To+y6joU6WqRU1aNP5zUZKRWKTHAzwnDerhgWKALgr21ULXi7CZT4uZghSdGdMfOs7nYdz4fh9OKkF1ShQcj/XjaOJkMlhsiAN8czUSBrgaO1pYI8zHtU4apbaiUCozp5Y4xvdxRXafHgZQCHE4rwrGMYpy5UoaKWj0ul1ThcknVTdejtlAiyN0ewd4O6OOlRbCXA3p7Osj6rCKVUoHxfT0Q4GKLr49kIrO4Ch/tTsHDg/3gw+tCkQlQCCGa+LtDvsrKyqDValFaWgoHBwep45AJqNMbMOqdPbhcUoUpoV6I6tZF6khm4cHI9rl6cFMXrWsuIQR0NfUoqqhFVa0eVXV6GK5+VKqUClhbqmCjtoCjjSVsNRZmPdJXqKvBl4cykFNWDUuVAg8M8kVvz9Z/trbX+4M6r+b8/ubIDZm9rYmXcbmkCi52Ggz046gNNRxEa29lCXsr7ma5lS52Gjwxohu+OpKB87k6rP8tHVPDvGU7txd1DjwCjMya3iCwak8qAGD+8IB2vVAakVxpLFWYNdgfg/ydIQBsTbqM+IuFUsciM8ZPcjJrP5/KxsWCCmitLfHQYD+p4xB1WiqlAtPCvDC8hwsA4H/Hr2B/SoHEqchcsdyQ2RJCYOXuhlGbuUP9O+XpuUSmRKFQYEJfD4wKcgUA/HQyG/vO50uciswRyw2ZrV3n8nA2uwy2ahXmDPGXOg6RLCgUCtzR2x1jezVMOrvtdA52ncuTOBWZG5YbMktCCHywKwUA8PBgP15GnqgNKRQKjO3tjnF93AEAO8/m4mAqd1FRx2G5IbN0IKUQSZkl0FgoMW94N6njEMnSqCA33HG14Px4IhvHs0qkDURmg+WGzNL7uy4AAGZG+MLV3rTn9iHqzEb1dEVUty4QAL49moWUPJ3UkcgMsNyQ2Tl0sRCH04qgVinx5MjuUschkjWFQoHJ/TwR4q2FXgisP5R+y6s+E7UWyw2ZnQ93NxxrM31gV3horSROQyR/SoUC0wd0RXdXW9TWGxB78BIKdTVSxyIZY7khs5KYUYxfLxTAQqngqA1RB7JQKfFQpB+8tFaoqKnHF/GXUFWrlzoWyRTLDZmVD6+eIXV3uDcn+CPqYFaWKjwyxB9aa0sU6Gqx8UiGcc4uorbEckNm49TlUsSdy4NSATw9OlDqOERmyd7KErMG+8FSpcCFPB22ncqROhLJEMsNmY2VV4+1mRLqhQAXW4nTEJkvL0dr3DfABwCwP6UACenFEiciuWG5IbNwPrccP1/9C3EBR22IJBfircWYq1cx3pp0GemFFRInIjlhuSGzcG3UZmKwB3q620uchogAYEwvN/TxdIDeILD+UAZKq+qkjkQywXJDspdWUIH/Hb8CAFg4hqM2RKZCqVA0XJLBoeEMqo2HM6A38ABjaj2WG5K9j3anwCCAsb3c0NdLK3UcIvoDjYUKD0X6QmOhRHpRJbaf5gHG1HosNyRrmUWV2JJ4GQBHbYhMVRc7DaYP6Aqg4QDjU5dLJU5EnR3LDcnah7tSUG8QGN7DBeG+TlLHIaIb6OOlxfBAFwDAd8eykFbAA4yp5VhuSLbSCyvw7bEsAMCz0T0lTkNEtzKurwf8u9igpt6Ap9Yn8ArG1GIsNyRbH+xKgd4gMCrIFQP8OGpDZOpUSgUeiPCFncYC53LKsfT7U1JHok6K5YZkKa2gAps5akPU6ThYWWLGIB8oFcCmhCx8n3RZ6kjUCbHckCx9EHfBeIZUmI+j1HGIqBm6u9ph4ZgeAIC/bjmFzKJKiRNRZ8NyQ7KTkqfD1qt/7T13B0dtiDqjP48JxEA/J5TX1OPPGxNRpzdIHYk6EZYbkp33r47a3NHHHcHevK4NUWdkoVLivQfCYG9lgcSMEry387zUkagTYbkhWbmQW47/nWi4GvGz0T0kTkNErdHVyQZv3tMPAPDRnlQcTCmQOBF1Fiw3JCvv7bwAIRrmkOLViIk6v8n9PDEzwgdCAM9+nYSiilqpI1EnwHJDsnEyqxQ/nsyGQgE8w1EbItlYemdfBLrZIa+8Bi9sOg4hOP8U3RzLDcnG29vPAQDuDvNGLw8HidMQUVuxVqvw/gPhUFsoEXcuD18cvCR1JDJxLDckCwdTCvDrhQJYqhQ8Q4pIhvp4OeCVSb0BAP/8+RzOXCmTOBGZMpMoNytXroS/vz+srKwQGRmJw4cP33DZ2NhYKBSKRjcrK6sOTEumRgiBt7YnAwAejPCFj7ONxImIqD3MjvJDdG831NYb8OeNiZyegW5I8nLz9ddfY9GiRVi2bBmOHTuG0NBQjB8/Hnl5eTd8joODA7Kzs4239PT0DkxMpmb76VwczyyBjVplvPAXEcmPQqHAW/f2g6u9Bil5OsT8fFbqSGSiJC83K1aswPz58zF37lz06dMHq1evho2NDdasWXPD5ygUCnh4eBhv7u7uHZiYTEm93oB//dIwavPYsAC42mskTkRE7amLnQbLp4cCANbFpyPubK7EicgUSVpuamtrkZCQgOjoaON9SqUS0dHRiI+Pv+HzdDod/Pz84OPjg6lTp+L06dMdEZdM0ObEy0jJ08HRxhLzR3STOg4RdYARPV3x2LAAAMCL355AXnm1xInI1EhabgoKCqDX668beXF3d0dOTk6TzwkKCsKaNWvw/fffY/369TAYDBgyZAiysrKaXL6mpgZlZWWNbiQP1XV6vLej4aqlC0YFwsHKUuJERNRRXhgfhF4e9iisqMULm07w9HBqRPLdUs0VFRWF2bNnIywsDCNHjsTmzZvh6uqKjz/+uMnlY2JioNVqjTcfH58OTkzt5YuDl3CltBqeWivMivKTOg4RdSArSxXenxkOjYUSe8/n8/RwakTScuPi4gKVSoXc3Mb7THNzc+Hh4XFb67C0tER4eDhSUlKafHzJkiUoLS013jIzM1udm6RXqKvBh7sa/s0X3dETVpYqiRMRUUfr6W6PVyb/fnp4ck65xInIVEhabtRqNQYMGIC4uDjjfQaDAXFxcYiKirqtdej1epw8eRKenp5NPq7RaODg4NDoRp3fezsvoLymHsHeDri3f1ep4xCRRGYN9sOYXldPD/8qEdV1PD2cTGC31KJFi/Dpp5/iiy++wNmzZ/HUU0+hoqICc+fOBQDMnj0bS5YsMS7/+uuv45dffsHFixdx7NgxPPzww0hPT8e8efOkegnUwS7klmPD4QwAwCuT+kCpVEiciIikolAo8PZ9/eBip0Zybjne2nZO6khkAiykDjBjxgzk5+dj6dKlyMnJQVhYGLZt22Y8yDgjIwNK5e8drLi4GPPnz0dOTg6cnJwwYMAAHDx4EH369JHqJVAH++dPZ6E3CIzr446o7l2kjkNEEnOx0+Cd6aGYu/YI1h64hJE9XTEqyE3qWCQhhTCzQ8zLysqg1WpRWlrKXVSd0L7z+Zi95jAslArsWDQSAS62bbr+DYcy2nR9dGMPRvq2y3r5bygPLXl//O2/pxF78BJc7DTY9uxwuNjxuldy0pzf35LvliK6XXqDwD9+bLgi6ewo/zYvNkTUuf1lYi8EudujQFeDl77l6eHmjOWGOo2vj2QiObccWmtL/HlsoNRxiMjEWFmq8O+ZYcbZw9dzFM9ssdxQp1BaWYflV6dZeGZsDzjaqCVORESmqJeHA/4yoRcA4O8/nEFKHk8PN0csN9Qp/OuXZBRW1CLQzQ4PD+YF+4joxuYO9cfInq6oqTfgT18loaaep4ebG5YbMnkns0qx/lDDzO9vTA2G2oJvWyK6MYVCgXem94OzrRpns8vwr+3JUkeiDsbfEmTSDAaBv35/CkIAU8O8eOo3Ed0WN3srvHNfPwDAp7+mYf+FAokTUUdiuSGTtvFIJo5nlsBOY4FXJvWWOg4RdSJje7tj1tXd2Iu+SUJRRa3EiaijsNyQySqqqMXb2xuuNvrcHT3h5mAlcSIi6mxentQbgW52yCuvwV++4+nh5oLlhkzW29vOoaSyDr087PEIZ/0mohawVqvw7wfCoFYp8cuZXPznt3SpI1EHYLkhk5SQXoyNRxpmcP/7tGBYqPhWJaKW6eulxV8mXjs9/CxOZpVKnIjaG39jkMmprtPjxW+PAwDuG9AVA/2dJU5ERJ3d3KH+GNfHHbV6AxZsOIay6jqpI1E7Yrkhk/N+3AWk5lfA1V6Dv07mQcRE1HoKhQLv3BeKrk7WyCiqxJLvTvL4GxljuSGTcjKrFB/vuwigYXcUr0RMRG1Fa2OJDx/sD0uVAj+ezMZ6Hn8jWyw3ZDJq6w144dvj0BsE7uznifF9PaSOREQyE+bjiJeuTs/wxg9nceoyj7+RI5YbMhkrd6fgXE45nG3VeO2uvlLHISKZemxYAKJ7Nxx/8+T6BJRU8vo3csNyQybhbHYZVu5OAQC8dldfdLHTSJyIiORKoVBg+fRQ+DrbIKu4Cn/emAS9gcffyAnLDUmutt6AxZuOo94gMK6PO+7s5yl1JCKSOa2NJVY/PABWlkrsO5+Pd3eclzoStSGWG5LcO9vP4fSVMjjaWOLv04KhUCikjkREZqCPlwPevKdh/qkPd6fgl9M5EieitsJyQ5Laez4fn/6aBgB4+95+nGKBiDrUtHBvzB3qDwBY9M1xpObrpA1EbYLlhiSTX16D579puFjfrMF+GMezo4hIAi9P6o0If2foaurxxH8SUM4L/HV6LDckCYNBYPGm4yjQ1SDI3R6v8GJ9RCQRS5USHz4UDncHDVLydPjTV4mo1xukjkWtwHJDklhzIA17z+dDY6HE+zPDYWWpkjoSEZkxN3srfDp7IKwsldiTnI+//3hW6kjUCiw31OFOZpXirW3nAAB/vbMPgjzsJU5ERAT06+qId+8PAwDEHryE/8RfkjQPtRzLDXWoAl0NnvjPUdTpBe7o446HI32ljkREZDQxxBMvjA8CAPztf2ew93y+xImoJVhuqMPU6Q14+stjuFJajQAXW/xreihP+yYik/P0qO64t39X6A0CC788hvO55VJHomZiuaEO88YPZ3A4rQh2Ggt8OnsAtNaWUkciIrqOQqHAP+8JRoS/M8pr6jFnzWFcKamSOhY1A8sNdYivj2RgXXzDDLzvzghDoBuPsyEi06WxUOHjWQPQ3dUWV0qrMevzQyiq4BxUnQXLDbW7hPRi/HXrKQDAojt64o4+7hInIiK6NSdbNf7zWCS8tFZIza/AnLWHoauplzoW3QaWG2pXGYWVeOI/CajTC0zo64GFowOljkREdNu8HK2x7rFIONlY4kRWKR5fdxQ19XqpY9EtsNxQu8kvr8GsNYdQoKtBLw97/Ov+UCiVPICYiDqXQDc7xM6NgK1ahYOphXjmqyRe5M/EsdxQuyivrsOctYeRXlgJH2drrHs0AnYaC6ljERG1SKiPIz6ZPRBqlRLbTufgma+TUMeCY7JYbqjNVdfp8fi6BJy+UgYXOzXWPRrJCTGJqNMbGuiClQ/1h6VKgR9PZONPGxJRW8+CY4pYbqhN6Q0Cz32dhPiLhbDTWCB2bgQCXGyljkVE1Cbu6OOOj2cNMI7gPP3lMR6DY4JYbqjN6A0CL357Aj+fyoFapcQnswYg2FsrdSwiojY1ppc7Pn1kIDQWSuw8m4sn/5OA6joWHFPCckNtQm8QeGHTcXx3LAsqpQL/fiAMQwJdpI5FRNQuRvZ0xZo5g2BlqcTu5HzMXXsEpVV1Useiq1huqNXq9AY893USNidehkqpwPsPhGNiiKfUsYiI2tXQQBfjWVTxFwsxffVBXOaVjE0Cyw21SlWtHvPXHcV/j1+BhVKBD2aGY3I/FhsiMg+Du3XBN09Gwd1Bg/O5OkxbeQCnLpdKHcvssdxQixVV1OLhzw9hT3I+rCyV+HT2QEziiA0RmZm+XlpseXoogtztkV9eg/s/jsfuc3lSxzJrLDfUIhfzdbjnowNISC+GvZUF1j8WidG93KSORUQkCS9Ha2x6KgrDAl1QWavHY18cwao9qTAYhNTRzBLLDTXb/gsFuGfVQVwqrERXJ2tsfmoIBvo7Sx2LiEhSDlaWWDt3EO4f2BUGAby17RzmrzuKkkpOuNnRWG7otgkh8PHeVMxecwgllXUI83HElqeHooc7Z/gmIgIAS5USb93bDzH3hEBtoUTcuTxMfn8/jmeWSB3NrLDc0G0pqazFE/9JQMzP52AQwPQBXbHx8cFwtddIHY2IyKQoFArMjPDF5qeGwK+LDS6XVOG+1Qfx+f407qbqICw3dEuHLhZi0r9/xS9ncqFWKfHGtGC8fV8/WFmqpI5GRGSygr21+N+fhmF8X3fU6QXe+OEMZnwSj4v5OqmjyR7LDd1QVa0er//vDB749DdcKa1GgIstNj89BLMG+0Gh4OzeRES34mBlidUPD8Ab04Jhq1bhyKViTPz3r/h4bypnFm9HLDfUpN3n8jDx3/uw5kAahADuH9gV//vTME6nQETUTAqFArMG+2H7cyMwvIcLauoNiPn5HO5ddRBJPBanXVhIHYBMy6WCCrz+wxnsunqNBncHDd68tx9GB/E0byKi1ujqZIN1j0ZgU0IW3vjhDI5nlWLaygO4K9QLL04IQlcnG6kjygbLDQEAdDX1+Gh3Cj77NQ21egMslAo8OiwAfxoTCHsrS6njERHJgkKhwP0DfTCypyve3paMzYlZ+O/xK9h2Ogdzh/pjwehAOPAzt9VYbsxcaWUdYg9ewpoDacZJ30b0dMXSO/sg0M1O4nRERPLk7mCF5feHYu5Qf/zzp7M4mFqIj/dexIZDGXh4sB/mDvWHm72V1DE7LZYbM1VUUYvP91/EuoPpKK+pBwB0c7XFkom9Ed3bjQcMExF1gGBvLb6cF4ndyXmI+ekcLuTpsGpPKj7fn4b7BnTF48O7wd/FVuqYnQ7LjRkRQuDIpWJsPJyBH09mo6a+4Uj9Xh72WDgmEBODPaFSstQQEXUkhUKBMb3cMaqnG3aezcWqvalIzCjBhkMZ+OpwBob3cMX9A7vijj7u0FjwEhy3g+XGDOSVVeO/x6/gq8MZSM2vMN7fr6sWC0cHIrq3O5QsNUREklIqFRjX1wN39HHH4bQirN6bit3J+dh3vuHmaGOJaWHeuDvcG/26ajnCfhMsNzKVXliB7adzsO1UDhIzSyCuXhTTRq3CXaFeeCDCF6H84SAiMjkKhQKR3bogslsXXCqowLcJWfg2IQs5ZdWIPXgJsQcvwVNrhTv6uOOOPu6IDOgCtQWv7PJHLDcyUVRRi8NphfjtYhEOphbgfG7jK2CG+zpi+gAf3BXmBTsN/9mJiDoDfxdbLB4fhOfu6In9KQXYdDQTu87lIbu0Guvi07EuPh32VhaIDHDG4G5dEBnQBX28HMz+EAOT+C23cuVKvPPOO8jJyUFoaCg++OADRERE3HD5TZs24dVXX8WlS5fQo0cPvPXWW5g0aVIHJpZWdZ0eyTnlOHWlFKculyExoxjncsobLaNSKhDVrQvG93XHHX084KHlUfdERJ2VSqnAyJ6uGNnTFdV1ehxMLcCOM7nYcSYXBbpa7Dybh51nG65PZm9lgXBfJwR7OSDYW4tgLy18nK3NaqRe8nLz9ddfY9GiRVi9ejUiIyPx3nvvYfz48UhOToab2/UXjjt48CBmzpyJmJgY3HnnndiwYQOmTZuGY8eOITg4WIJX0D6EECisqEVmUSUu5lcgraACFwt0uJhfgZQ8HeqbmHytp7sdBnfrgsHdumBI9y5wtFFLkJyIiNqTlaUKY3q5Y0wvd/x9msCpy6U4dHXk/khaEcqr643H6Vxjb2WB7q526OZq2/BfF1v4drGBp9YaTjaWsis+CiGEpFOURkZGYtCgQfjwww8BAAaDAT4+PvjTn/6Ev/zlL9ctP2PGDFRUVOCHH34w3jd48GCEhYVh9erVt/x+ZWVl0Gq1KC0thYODQ9u9kFswGASq6vSoqK1HWVUdiirqUFRRi6KKWhRX1hr/P7u0Ctml1cgurUZt/Y3nHXGysWxo5N5ahHhrERHgDBc7ztDdWhsOZUgdwWw8GOnbLuvlv6E8tNf7Q+70BoEzV8pwPKsEp6+O7ifnlKP2JvNYqS2U8HCwarhpG24udmo4WquhtbGEo7UlHG3U0FpbwtHGEhoLpSRlqDm/vyUduamtrUVCQgKWLFlivE+pVCI6Ohrx8fFNPic+Ph6LFi1qdN/48eOxdevW9ox6S+dzy/HO9mRU1TYUmGv/razRo7JWj6o6fbPXqVAAbvYaBLjYotvVph3gYoteng7w0lrJrmkTEVHrqJQKhHTVIqTr7/MA1tYbkJqvu7oXoOG/qQUVuFxciQJdLWrrDcgoqkRGUeVtfQ+lArBRW8DKUgVrtRI2lhawUqtgbamEtaUKNmoLdHezw6I7erbXy7wlSctNQUEB9Ho93N3dG93v7u6Oc+fONfmcnJycJpfPyclpcvmamhrU1NQYvy4tLQXQ0ADbUm5BMbYnpt1yOYUCsNdYwNlWDUcbNRxtLOFkYwlHWzWcrC3h7mAF96sN2s3B6gZHwNehvLyuTfNTg8qK8lsvRG2irX8Gr+G/oTy01/vDXHnbAt62thju3/iCgDX1euSX1SC3vBq5ZTXIK6tGTlkNSiprUVpVi9Kqhr0NZVV1KK2uh94gYABQVg3c7F8ozMcR8yI92vQ1XHtP3M4OJ8mPuWlvMTExeO21166738fHR4I0RHTNfKkDkEnj+6NzywSgfb591l1eXg6tVnvTZSQtNy4uLlCpVMjNzW10f25uLjw8mm58Hh4ezVp+yZIljXZjGQwGFBUVoUuXLpLt1ikrK4OPjw8yMzM79LgfU8Rt0Ri3x++4LX7HbfE7bovfmdu2EEKgvLwcXl5et1xW0nKjVqsxYMAAxMXFYdq0aQAaykdcXBwWLlzY5HOioqIQFxeHZ5991njfjh07EBUV1eTyGo0GGk3jA20dHR3bIn6rOTg4mMUb8nZwWzTG7fE7bovfcVv8jtvid+a0LW41YnON5LulFi1ahEceeQQDBw5EREQE3nvvPVRUVGDu3LkAgNmzZ8Pb2xsxMTEAgGeeeQYjR47E8uXLMXnyZGzcuBFHjx7FJ598IuXLICIiIhMhebmZMWMG8vPzsXTpUuTk5CAsLAzbtm0zHjSckZEBpfL3g2qHDBmCDRs24K9//Stefvll9OjRA1u3bpXVNW6IiIio5SQvNwCwcOHCG+6G2rNnz3X3TZ8+HdOnT2/nVO1Ho9Fg2bJl1+0uM0fcFo1xe/yO2+J33Ba/47b4HbfFjUl+ET8iIiKitsRpRImIiEhWWG6IiIhIVlhuiIiISFZYbiSwcuVK+Pv7w8rKCpGRkTh8+LDUkZolJiYGgwYNgr29Pdzc3DBt2jQkJyc3WmbUqFFQKBSNbk8++WSjZTIyMjB58mTY2NjAzc0NL7zwAurr6xsts2fPHvTv3x8ajQaBgYGIjY29Lo+U2/Nvf/vbda+zV69exserq6uxYMECdOnSBXZ2drj33nuvuwilHLYDAPj7+1+3LRQKBRYsWABA3u+Jffv2YcqUKfDy8oJCobhurjshBJYuXQpPT09YW1sjOjoaFy5caLRMUVERHnroITg4OMDR0RGPPfYYdDpdo2VOnDiB4cOHw8rKCj4+Pnj77bevy7Jp0yb06tULVlZWCAkJwU8//dTsLK1xs21RV1eHl156CSEhIbC1tYWXlxdmz56NK1euNFpHU++lN998U1bbAgDmzJlz3eucMGFCo2Xk8r7ocII61MaNG4VarRZr1qwRp0+fFvPnzxeOjo4iNzdX6mi3bfz48WLt2rXi1KlTIikpSUyaNEn4+voKnU5nXGbkyJFi/vz5Ijs723grLS01Pl5fXy+Cg4NFdHS0SExMFD/99JNwcXERS5YsMS5z8eJFYWNjIxYtWiTOnDkjPvjgA6FSqcS2bduMy0i9PZctWyb69u3b6HXm5+cbH3/yySeFj4+PiIuLE0ePHhWDBw8WQ4YMkd12EEKIvLy8Rtthx44dAoDYvXu3EELe74mffvpJvPLKK2Lz5s0CgNiyZUujx998802h1WrF1q1bxfHjx8Vdd90lAgICRFVVlXGZCRMmiNDQUPHbb7+JX3/9VQQGBoqZM2caHy8tLRXu7u7ioYceEqdOnRJfffWVsLa2Fh9//LFxmQMHDgiVSiXefvttcebMGfHXv/5VWFpaipMnTzYrS3tti5KSEhEdHS2+/vprce7cOREfHy8iIiLEgAEDGq3Dz89PvP76643eK3/8fJHDthBCiEceeURMmDCh0essKipqtIxc3hcdjeWmg0VERIgFCxYYv9br9cLLy0vExMRImKp18vLyBACxd+9e430jR44UzzzzzA2f89NPPwmlUilycnKM961atUo4ODiImpoaIYQQL774oujbt2+j582YMUOMHz/e+LXU23PZsmUiNDS0ycdKSkqEpaWl2LRpk/G+s2fPCgAiPj5eCCGf7dCUZ555RnTv3l0YDAYhhPm8J/7/LzGDwSA8PDzEO++8Y7yvpKREaDQa8dVXXwkhhDhz5owAII4cOWJc5ueffxYKhUJcvnxZCCHERx99JJycnIzbQgghXnrpJREUFGT8+v777xeTJ09ulCcyMlI88cQTt52lLTX1C/3/O3z4sAAg0tPTjff5+fmJd99994bPkcu2eOSRR8TUqVNv+By5vi86AndLdaDa2lokJCQgOjraeJ9SqUR0dDTi4+MlTNY612Zad3Z2bnT/l19+CRcXFwQHB2PJkiWorKw0PhYfH4+QkJBGM7yPHz8eZWVlOH36tHGZP26ra8tc21amsj0vXLgALy8vdOvWDQ899BAyMjIAAAkJCairq2uUr1evXvD19TXmk9N2+KPa2lqsX78ejz76aKM53MzlPfFHaWlpyMnJaZRJq9UiMjKy0fvA0dERAwcONC4THR0NpVKJQ4cOGZcZMWIE1Gq1cZnx48cjOTkZxcXFxmVutn1uJ0tHKy0thUKhuG5anDfffBNdunRBeHg43nnnnUa7J+W0Lfbs2QM3NzcEBQXhqaeeQmFhofExc35ftJZJXMTPXBQUFECv1zf68AYAd3d3nDt3TqJUrWMwGPDss89i6NChja4S/eCDD8LPzw9eXl44ceIEXnrpJSQnJ2Pz5s0AgJycnCa3w7XHbrZMWVkZqqqqUFxcLPn2jIyMRGxsLIKCgpCdnY3XXnsNw4cPx6lTp5CTkwO1Wn3dh7a7u/stX+O1x262jClth/9v69atKCkpwZw5c4z3mct74v+7lr2pTH98XW5ubo0et7CwgLOzc6NlAgICrlvHtcecnJxuuH3+uI5bZelI1dXVeOmllzBz5sxGcyP9+c9/Rv/+/eHs7IyDBw9iyZIlyM7OxooVKwDIZ1tMmDAB99xzDwICApCamoqXX34ZEydORHx8PFQqldm+L9oCyw21yoIFC3Dq1Cns37+/0f2PP/648f9DQkLg6emJsWPHIjU1Fd27d+/omO1m4sSJxv/v168fIiMj4efnh2+++QbW1tYSJpPW559/jokTJzaavddc3hN0e+rq6nD//fdDCIFVq1Y1emzRokXG/+/Xrx/UajWeeOIJxMTEyOpqvA888IDx/0NCQtCvXz90794de/bswdixYyVM1vlxt1QHcnFxgUqluu5smdzcXHh4eEiUquUWLlyIH374Abt370bXrl1vumxkZCQAICUlBQDg4eHR5Ha49tjNlnFwcIC1tbVJbk9HR0f07NkTKSkp8PDwQG1tLUpKSm6YT47bIT09HTt37sS8efNuupy5vCeufd+bZfLw8EBeXl6jx+vr61FUVNQm75U/Pn6rLB3hWrFJT0/Hjh07bjmjdWRkJOrr63Hp0iUA8toWf9StWze4uLg0+pkwp/dFW2K56UBqtRoDBgxAXFyc8T6DwYC4uDhERUVJmKx5hBBYuHAhtmzZgl27dl03JNqUpKQkAICnpycAICoqCidPnmz0g3vtQ65Pnz7GZf64ra4tc21bmeL21Ol0SE1NhaenJwYMGABLS8tG+ZKTk5GRkWHMJ8ftsHbtWri5uWHy5Mk3Xc5c3hMBAQHw8PBolKmsrAyHDh1q9D4oKSlBQkKCcZldu3bBYDAYS2BUVBT27duHuro64zI7duxAUFAQnJycjMvcbPvcTpb2dq3YXLhwATt37kSXLl1u+ZykpCQolUrjLhq5bIv/LysrC4WFhY1+JszlfdHmpD6i2dxs3LhRaDQaERsbK86cOSMef/xx4ejo2OgMEVP31FNPCa1WK/bs2dPoFMbKykohhBApKSni9ddfF0ePHhVpaWni+++/F926dRMjRowwruPaab/jxo0TSUlJYtu2bcLV1bXJ035feOEFcfbsWbFy5comT/uVcns+//zzYs+ePSItLU0cOHBAREdHCxcXF5GXlyeEaDgV3NfXV+zatUscPXpUREVFiaioKNlth2v0er3w9fUVL730UqP75f6eKC8vF4mJiSIxMVEAECtWrBCJiYnGM4DefPNN4ejoKL7//ntx4sQJMXXq1CZPBQ8PDxeHDh0S+/fvFz169Gh0ym9JSYlwd3cXs2bNEqdOnRIbN24UNjY2153ya2FhIf71r3+Js2fPimXLljV5yu+tsrTXtqitrRV33XWX6Nq1q0hKSmr0+XHtbJ+DBw+Kd999VyQlJYnU1FSxfv164erqKmbPni2rbVFeXi4WL14s4uPjRVpamti5c6fo37+/6NGjh6iurjauQy7vi47GciOBDz74QPj6+gq1Wi0iIiLEb7/9JnWkZgHQ5G3t2rVCCCEyMjLEiBEjhLOzs9BoNCIwMFC88MILja5pIoQQly5dEhMnThTW1tbCxcVFPP/886Kurq7RMrt37xZhYWFCrVaLbt26Gb/HH0m5PWfMmCE8PT2FWq0W3t7eYsaMGSIlJcX4eFVVlXj66aeFk5OTsLGxEXfffbfIzs5utA45bIdrtm/fLgCI5OTkRvfL/T2xe/fuJn8mHnnkESFEw6m2r776qnB3dxcajUaMHTv2um1UWFgoZs6cKezs7ISDg4OYO3euKC8vb7TM8ePHxbBhw4RGoxHe3t7izTffvC7LN998I3r27CnUarXo27ev+PHHHxs9fjtZ2mtbpKWl3fDz49r1kBISEkRkZKTQarXCyspK9O7dW/zzn/9s9AtfDtuisrJSjBs3Tri6ugpLS0vh5+cn5s+ff10Jl8v7oqNxVnAiIiKSFR5zQ0RERLLCckNERESywnJDREREssJyQ0RERLLCckNERESywnJDREREssJyQ0RERLLCckNERESywnJDRJ1abGwsHB0dpY5BRCaE5YaIJJWfn4+nnnoKvr6+0Gg08PDwwPjx43HgwAGpoxFRJ2UhdQAiMm/33nsvamtr8cUXX6Bbt27Izc1FXFwcCgsLOyxDbW0t1Gp1h30/ImpfHLkhIsmUlJTg119/xVtvvYXRo0fDz88PERERWLJkCe666y4AwIoVKxASEgJbW1v4+Pjg6aefhk6nu+E6U1NTMXXqVLi7u8POzg6DBg3Czp07Gy3j7++PN954A7Nnz4aDgwMef/xxjBkzBgsXLmy0XH5+PtRqNeLi4tr+xRNRu2G5ISLJ2NnZwc7ODlu3bkVNTU2TyyiVSrz//vs4ffo0vvjiC+zatQsvvvjiDdep0+kwadIkxMXFITExERMmTMCUKVOQkZHRaLl//etfCA0NRWJiIl599VXMmzcPGzZsaJRj/fr18Pb2xpgxY9rmBRNRh+Cs4EQkqe+++w7z589HVVUV+vfvj5EjR+KBBx5Av379mlz+22+/xZNPPomCggIADQcUP/vssygpKbnh9wgODsaTTz5pHJnx9/dHeHg4tmzZYlymuroaXl5eWL16Ne6//34AQGhoKO655x4sW7asjV4tEXUEjtwQkaTuvfdeXLlyBf/9738xYcIE7NmzB/3790dsbCwAYOfOnRg7diy8vb1hb2+PWbNmobCwEJWVlU2uT6fTYfHixejduzccHR1hZ2eHs2fPXjdyM3DgwEZfW1lZYdasWVizZg0A4NixYzh16hTmzJnT5q+ZiNoXyw0RSc7Kygp33HEHXn31VRw8eBBz5szBsmXLcOnSJdx5553o168fvvvuOyQkJGDlypUAGg4CbsrixYuxZcsW/POf/8Svv/6KpKQkhISEXLe8ra3tdc+dN28eduzYgaysLKxduxZjxoyBn59f279gImpXPFuKiExOnz59sHXrViQkJMBgMGD58uVQKhv+Fvvmm29u+twDBw5gzpw5uPvuuwE0jORcunTptr5vSEgIBg4ciE8//RQbNmzAhx9+2KrXQUTSYLkhIskUFhZi+vTpePTRR9GvXz/Y29vj6NGjePvttzF16lQEBgairq4OH3zwAaZMmYIDBw5g9erVN11njx49sHnzZkyZMgUKhQKvvvoqDAbDbWeaN28eFi5cCFtbW2NBIqLOhbuliEgydnZ2iIyMxLvvvosRI0YgODgYr776KubPn48PP/wQoaGhWLFiBd566y0EBwfjyy+/RExMzE3XuWLFCjg5OWHIkCGYMmUKxo8fj/79+992ppkzZ8LCwgIzZ86ElZVVa18iEUmAZ0sREf3BpUuX0L17dxw5cqRZpYiITAfLDRERgLq6OhQWFmLx4sVIS0vj9A9EnRh3SxERoeFAZE9PTxw5cuSWx/UQkWnjyA0RERHJCkduiIiISFZYboiIiEhWWG6IiIhIVlhuiIiISFZYboiIiEhWWG6IiIhIVlhuiIiISFZYboiIiEhWWG6IiIhIVv4PHpiC8Jae3acAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['Before Transformation','After Transformation']\n",
    "sns.distplot(df['Salary'], bins = 10, kde = True)\n",
    "sns.displot(np.log(df['Salary']), bins = 10, kde = True)\n",
    "plt.title('After Transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0cbb93ea-6af1-494b-a126-64866c77f1d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smf.qqplot(df['Salary'], line = 'r')\n",
    "plt.title('No transformation')\n",
    "smf.qqplot(np.log(df['Salary']), line = 'r')\n",
    "plt.title('Log transformation')\n",
    "smf.qqplot(np.sqrt(df['Salary']), line = 'r')\n",
    "plt.title('Square root transformation')\n",
    "smf.qqplot(np.cbrt(df['Salary']), line = 'r')\n",
    "plt.title('Cube root transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87f093c6-b68c-45e3-b45e-c90c0adb891c",
   "metadata": {},
   "source": [
    "**Important Note:**\n",
    "+ We only Perform any data transformation when the data is skewed or not normal distribution N(0,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33245b85-e1ae-4d55-b81c-908d01600294",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# <font color='Green'>Step-7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4013709-9421-46a4-b4d8-fe09328a1b0f",
   "metadata": {},
   "source": [
    "**Fitting a Linear Regression Model**\n",
    "\n",
    "**Using Ordinary least squares (OLS) regression**\n",
    "\n",
    "It is a statistical method of analysis that estimates the relationship between one or more independent variables and a dependent variable; the method estimates the relationship by minimizing the sum of the squares in the difference between the observed and predicted values of the dependent variable configured as a straight line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "96f27fab-184b-4ed7-af93-ddeca301f0d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as sm\n",
    "model = sm.ols('Salary~YearsExperience', data = df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5b0a408f-a412-40da-96dc-593afef51f27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Salary</td>      <th>  R-squared:         </th> <td>   0.957</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.955</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   622.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 07 Mar 2024</td> <th>  Prob (F-statistic):</th> <td>1.14e-20</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>02:49:50</td>     <th>  Log-Likelihood:    </th> <td> -301.44</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>   606.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    28</td>      <th>  BIC:               </th> <td>   609.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>       <td> 2.579e+04</td> <td> 2273.053</td> <td>   11.347</td> <td> 0.000</td> <td> 2.11e+04</td> <td> 3.04e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>YearsExperience</th> <td> 9449.9623</td> <td>  378.755</td> <td>   24.950</td> <td> 0.000</td> <td> 8674.119</td> <td> 1.02e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.140</td> <th>  Durbin-Watson:     </th> <td>   1.648</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.343</td> <th>  Jarque-Bera (JB):  </th> <td>   1.569</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.363</td> <th>  Prob(JB):          </th> <td>   0.456</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.147</td> <th>  Cond. No.          </th> <td>    13.2</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &      Salary      & \\textbf{  R-squared:         } &     0.957   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.955   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     622.5   \\\\\n",
       "\\textbf{Date:}             & Thu, 07 Mar 2024 & \\textbf{  Prob (F-statistic):} &  1.14e-20   \\\\\n",
       "\\textbf{Time:}             &     02:49:50     & \\textbf{  Log-Likelihood:    } &   -301.44   \\\\\n",
       "\\textbf{No. Observations:} &          30      & \\textbf{  AIC:               } &     606.9   \\\\\n",
       "\\textbf{Df Residuals:}     &          28      & \\textbf{  BIC:               } &     609.7   \\\\\n",
       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                         & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}       &    2.579e+04  &     2273.053     &    11.347  &         0.000        &     2.11e+04    &     3.04e+04     \\\\\n",
       "\\textbf{YearsExperience} &    9449.9623  &      378.755     &    24.950  &         0.000        &     8674.119    &     1.02e+04     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  2.140 & \\textbf{  Durbin-Watson:     } &    1.648  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.343 & \\textbf{  Jarque-Bera (JB):  } &    1.569  \\\\\n",
       "\\textbf{Skew:}          &  0.363 & \\textbf{  Prob(JB):          } &    0.456  \\\\\n",
       "\\textbf{Kurtosis:}      &  2.147 & \\textbf{  Cond. No.          } &     13.2  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Salary   R-squared:                       0.957\n",
       "Model:                            OLS   Adj. R-squared:                  0.955\n",
       "Method:                 Least Squares   F-statistic:                     622.5\n",
       "Date:                Thu, 07 Mar 2024   Prob (F-statistic):           1.14e-20\n",
       "Time:                        02:49:50   Log-Likelihood:                -301.44\n",
       "No. Observations:                  30   AIC:                             606.9\n",
       "Df Residuals:                      28   BIC:                             609.7\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept        2.579e+04   2273.053     11.347      0.000    2.11e+04    3.04e+04\n",
       "YearsExperience  9449.9623    378.755     24.950      0.000    8674.119    1.02e+04\n",
       "==============================================================================\n",
       "Omnibus:                        2.140   Durbin-Watson:                   1.648\n",
       "Prob(Omnibus):                  0.343   Jarque-Bera (JB):                1.569\n",
       "Skew:                           0.363   Prob(JB):                        0.456\n",
       "Kurtosis:                       2.147   Cond. No.                         13.2\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc1c5841-1419-40e5-be9b-f94549226c2e",
   "metadata": {},
   "source": [
    "**As you can notice in the above model,**\n",
    "+ The R-squared and Adjusted R-squared scores are above 0.85.\n",
    "+ (It is a thumb rule to consider Adjusted R-squared to be greater than 0.8 for a good model for prediction)\n",
    "+ F-statitics is quite high as well and yes desire it to be higher\n",
    "+ But log-likelihood is quite very low far away from 0\n",
    "+ and AIC and BIC score are much higher for this model\n",
    "+ Lets Try some data transformation to check whether these scores can get any better than this.\n",
    "\n",
    "**Square Root transformation on data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "50b4c060-56d5-4599-be23-1a77bf75f7a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>np.sqrt(Salary)</td> <th>  R-squared:         </th> <td>   0.942</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.940</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   454.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 07 Mar 2024</td> <th>  Prob (F-statistic):</th> <td>7.58e-19</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>02:51:49</td>     <th>  Log-Likelihood:    </th> <td> -116.52</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>   237.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    28</td>      <th>  BIC:               </th> <td>   239.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>  103.5680</td> <td>    8.178</td> <td>   12.663</td> <td> 0.000</td> <td>   86.815</td> <td>  120.321</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.sqrt(YearsExperience)</th> <td>   75.6269</td> <td>    3.548</td> <td>   21.315</td> <td> 0.000</td> <td>   68.359</td> <td>   82.895</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.924</td> <th>  Durbin-Watson:     </th> <td>   1.362</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.630</td> <th>  Jarque-Bera (JB):  </th> <td>   0.801</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.087</td> <th>  Prob(JB):          </th> <td>   0.670</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.219</td> <th>  Cond. No.          </th> <td>    9.97</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}           & np.sqrt(Salary)  & \\textbf{  R-squared:         } &     0.942   \\\\\n",
       "\\textbf{Model:}                   &       OLS        & \\textbf{  Adj. R-squared:    } &     0.940   \\\\\n",
       "\\textbf{Method:}                  &  Least Squares   & \\textbf{  F-statistic:       } &     454.3   \\\\\n",
       "\\textbf{Date:}                    & Thu, 07 Mar 2024 & \\textbf{  Prob (F-statistic):} &  7.58e-19   \\\\\n",
       "\\textbf{Time:}                    &     02:51:49     & \\textbf{  Log-Likelihood:    } &   -116.52   \\\\\n",
       "\\textbf{No. Observations:}        &          30      & \\textbf{  AIC:               } &     237.0   \\\\\n",
       "\\textbf{Df Residuals:}            &          28      & \\textbf{  BIC:               } &     239.8   \\\\\n",
       "\\textbf{Df Model:}                &           1      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}         &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                                  & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}                &     103.5680  &        8.178     &    12.663  &         0.000        &       86.815    &      120.321     \\\\\n",
       "\\textbf{np.sqrt(YearsExperience)} &      75.6269  &        3.548     &    21.315  &         0.000        &       68.359    &       82.895     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  0.924 & \\textbf{  Durbin-Watson:     } &    1.362  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.630 & \\textbf{  Jarque-Bera (JB):  } &    0.801  \\\\\n",
       "\\textbf{Skew:}          &  0.087 & \\textbf{  Prob(JB):          } &    0.670  \\\\\n",
       "\\textbf{Kurtosis:}      &  2.219 & \\textbf{  Cond. No.          } &     9.97  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:        np.sqrt(Salary)   R-squared:                       0.942\n",
       "Model:                            OLS   Adj. R-squared:                  0.940\n",
       "Method:                 Least Squares   F-statistic:                     454.3\n",
       "Date:                Thu, 07 Mar 2024   Prob (F-statistic):           7.58e-19\n",
       "Time:                        02:51:49   Log-Likelihood:                -116.52\n",
       "No. Observations:                  30   AIC:                             237.0\n",
       "Df Residuals:                      28   BIC:                             239.8\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "============================================================================================\n",
       "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------\n",
       "Intercept                  103.5680      8.178     12.663      0.000      86.815     120.321\n",
       "np.sqrt(YearsExperience)    75.6269      3.548     21.315      0.000      68.359      82.895\n",
       "==============================================================================\n",
       "Omnibus:                        0.924   Durbin-Watson:                   1.362\n",
       "Prob(Omnibus):                  0.630   Jarque-Bera (JB):                0.801\n",
       "Skew:                           0.087   Prob(JB):                        0.670\n",
       "Kurtosis:                       2.219   Cond. No.                         9.97\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 = sm.ols('np.sqrt(Salary)~np.sqrt(YearsExperience)', data = df).fit()\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dad0eb2-f0da-48a9-b9b0-e65817580b28",
   "metadata": {},
   "source": [
    "**As you can notice in the above model,**\n",
    "+ The R-squared and Adjusted R-squared scores are above 0.85. but its has gotten less than previous model\n",
    "+ (It is a thumb rule to consider Adjusted R-squared to be greater than 0.8 for a good model for prediction)\n",
    "+ F-statitics has gotten a little lower for this model than previous.\n",
    "+ But log-likelihood got better than before close to 0 higher than previous model\n",
    "+ and AIC and BIC score are now much better for this model\n",
    "+ Lets Try some data transformation to check whether these scores can get any better than this.\n",
    "\n",
    "**Cuberoot transformation on Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2176d3df-ca15-4e54-9854-3abeee5dfd02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>np.cbrt(Salary)</td> <th>  R-squared:         </th> <td>   0.932</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.930</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   386.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 07 Mar 2024</td> <th>  Prob (F-statistic):</th> <td>6.37e-18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>02:53:34</td>     <th>  Log-Likelihood:    </th> <td> -50.589</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>   105.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    28</td>      <th>  BIC:               </th> <td>   108.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>   16.6603</td> <td>    1.300</td> <td>   12.811</td> <td> 0.000</td> <td>   13.996</td> <td>   19.324</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.cbrt(YearsExperience)</th> <td>   14.8963</td> <td>    0.758</td> <td>   19.659</td> <td> 0.000</td> <td>   13.344</td> <td>   16.448</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.386</td> <th>  Durbin-Watson:     </th> <td>   1.229</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.824</td> <th>  Jarque-Bera (JB):  </th> <td>   0.535</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.070</td> <th>  Prob(JB):          </th> <td>   0.765</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.361</td> <th>  Cond. No.          </th> <td>    12.0</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}           & np.cbrt(Salary)  & \\textbf{  R-squared:         } &     0.932   \\\\\n",
       "\\textbf{Model:}                   &       OLS        & \\textbf{  Adj. R-squared:    } &     0.930   \\\\\n",
       "\\textbf{Method:}                  &  Least Squares   & \\textbf{  F-statistic:       } &     386.5   \\\\\n",
       "\\textbf{Date:}                    & Thu, 07 Mar 2024 & \\textbf{  Prob (F-statistic):} &  6.37e-18   \\\\\n",
       "\\textbf{Time:}                    &     02:53:34     & \\textbf{  Log-Likelihood:    } &   -50.589   \\\\\n",
       "\\textbf{No. Observations:}        &          30      & \\textbf{  AIC:               } &     105.2   \\\\\n",
       "\\textbf{Df Residuals:}            &          28      & \\textbf{  BIC:               } &     108.0   \\\\\n",
       "\\textbf{Df Model:}                &           1      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}         &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                                  & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}                &      16.6603  &        1.300     &    12.811  &         0.000        &       13.996    &       19.324     \\\\\n",
       "\\textbf{np.cbrt(YearsExperience)} &      14.8963  &        0.758     &    19.659  &         0.000        &       13.344    &       16.448     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  0.386 & \\textbf{  Durbin-Watson:     } &    1.229  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.824 & \\textbf{  Jarque-Bera (JB):  } &    0.535  \\\\\n",
       "\\textbf{Skew:}          &  0.070 & \\textbf{  Prob(JB):          } &    0.765  \\\\\n",
       "\\textbf{Kurtosis:}      &  2.361 & \\textbf{  Cond. No.          } &     12.0  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:        np.cbrt(Salary)   R-squared:                       0.932\n",
       "Model:                            OLS   Adj. R-squared:                  0.930\n",
       "Method:                 Least Squares   F-statistic:                     386.5\n",
       "Date:                Thu, 07 Mar 2024   Prob (F-statistic):           6.37e-18\n",
       "Time:                        02:53:34   Log-Likelihood:                -50.589\n",
       "No. Observations:                  30   AIC:                             105.2\n",
       "Df Residuals:                      28   BIC:                             108.0\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "============================================================================================\n",
       "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------\n",
       "Intercept                   16.6603      1.300     12.811      0.000      13.996      19.324\n",
       "np.cbrt(YearsExperience)    14.8963      0.758     19.659      0.000      13.344      16.448\n",
       "==============================================================================\n",
       "Omnibus:                        0.386   Durbin-Watson:                   1.229\n",
       "Prob(Omnibus):                  0.824   Jarque-Bera (JB):                0.535\n",
       "Skew:                           0.070   Prob(JB):                        0.765\n",
       "Kurtosis:                       2.361   Cond. No.                         12.0\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = sm.ols('np.cbrt(Salary)~np.cbrt(YearsExperience)', data = df).fit()\n",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24a1f942-2e36-4cb8-9f31-1c5d797939cd",
   "metadata": {},
   "source": [
    "**Log Transformation on data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dbc7dfc7-a47a-43a4-8d55-a5403993c515",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>np.log(Salary)</td>  <th>  R-squared:         </th> <td>   0.905</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   267.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 07 Mar 2024</td> <th>  Prob (F-statistic):</th> <td>7.40e-16</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>02:54:31</td>     <th>  Log-Likelihood:    </th> <td>  23.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>  -42.42</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    28</td>      <th>  BIC:               </th> <td>  -39.61</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>                <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>               <td>   10.3280</td> <td>    0.056</td> <td>  184.868</td> <td> 0.000</td> <td>   10.214</td> <td>   10.442</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(YearsExperience)</th> <td>    0.5621</td> <td>    0.034</td> <td>   16.353</td> <td> 0.000</td> <td>    0.492</td> <td>    0.632</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.102</td> <th>  Durbin-Watson:     </th> <td>   0.988</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.950</td> <th>  Jarque-Bera (JB):  </th> <td>   0.297</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.093</td> <th>  Prob(JB):          </th> <td>   0.862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.549</td> <th>  Cond. No.          </th> <td>    5.76</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &  np.log(Salary)  & \\textbf{  R-squared:         } &     0.905   \\\\\n",
       "\\textbf{Model:}                  &       OLS        & \\textbf{  Adj. R-squared:    } &     0.902   \\\\\n",
       "\\textbf{Method:}                 &  Least Squares   & \\textbf{  F-statistic:       } &     267.4   \\\\\n",
       "\\textbf{Date:}                   & Thu, 07 Mar 2024 & \\textbf{  Prob (F-statistic):} &  7.40e-16   \\\\\n",
       "\\textbf{Time:}                   &     02:54:31     & \\textbf{  Log-Likelihood:    } &    23.209   \\\\\n",
       "\\textbf{No. Observations:}       &          30      & \\textbf{  AIC:               } &    -42.42   \\\\\n",
       "\\textbf{Df Residuals:}           &          28      & \\textbf{  BIC:               } &    -39.61   \\\\\n",
       "\\textbf{Df Model:}               &           1      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                                 & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}               &      10.3280  &        0.056     &   184.868  &         0.000        &       10.214    &       10.442     \\\\\n",
       "\\textbf{np.log(YearsExperience)} &       0.5621  &        0.034     &    16.353  &         0.000        &        0.492    &        0.632     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  0.102 & \\textbf{  Durbin-Watson:     } &    0.988  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.950 & \\textbf{  Jarque-Bera (JB):  } &    0.297  \\\\\n",
       "\\textbf{Skew:}          &  0.093 & \\textbf{  Prob(JB):          } &    0.862  \\\\\n",
       "\\textbf{Kurtosis:}      &  2.549 & \\textbf{  Cond. No.          } &     5.76  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:         np.log(Salary)   R-squared:                       0.905\n",
       "Model:                            OLS   Adj. R-squared:                  0.902\n",
       "Method:                 Least Squares   F-statistic:                     267.4\n",
       "Date:                Thu, 07 Mar 2024   Prob (F-statistic):           7.40e-16\n",
       "Time:                        02:54:31   Log-Likelihood:                 23.209\n",
       "No. Observations:                  30   AIC:                            -42.42\n",
       "Df Residuals:                      28   BIC:                            -39.61\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===========================================================================================\n",
       "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------------\n",
       "Intercept                  10.3280      0.056    184.868      0.000      10.214      10.442\n",
       "np.log(YearsExperience)     0.5621      0.034     16.353      0.000       0.492       0.632\n",
       "==============================================================================\n",
       "Omnibus:                        0.102   Durbin-Watson:                   0.988\n",
       "Prob(Omnibus):                  0.950   Jarque-Bera (JB):                0.297\n",
       "Skew:                           0.093   Prob(JB):                        0.862\n",
       "Kurtosis:                       2.549   Cond. No.                         5.76\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3 = sm.ols('np.log(Salary)~np.log(YearsExperience)', data = df).fit()\n",
    "model3.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50496eef-bdd1-4e91-bff9-58f301e445a4",
   "metadata": {},
   "source": [
    "Model Testing\n",
    "\n",
    "As Y = Beta0 + Beta1*(X)\n",
    "\n",
    "Finding Coefficient Parameters (Beta0 and Beta1 values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "42aa0b43-b4ca-46d1-a999-2c91675ba74b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept          25792.200199\n",
       "YearsExperience     9449.962321\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1b179bb-3f30-4342-a63a-1b0624a42769",
   "metadata": {},
   "source": [
    "Here, (Intercept) Beta0 value = 25792.20 & (YearsExperience) Beta1 value = 9449.96\n",
    "\n",
    "Hypothesis testing of X variable by finding test_statistics and P_values for Beta1 i.e if (P_value < α=0.05 ; Reject Null)\n",
    "\n",
    "Null Hypothesis as Beta1=0 (No Slope) and Alternate Hypthesis as Beta1≠0 (Some or significant Slope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "34330db9-160f-4c51-a9c2-ff990b6bba53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept          11.346940\n",
      "YearsExperience    24.950094\n",
      "dtype: float64 \n",
      " Intercept          5.511950e-12\n",
      "YearsExperience    1.143068e-20\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(model.tvalues,'\\n',model.pvalues)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aa5ced3-4f62-4d8e-a7a0-698b4a0b1c16",
   "metadata": {},
   "source": [
    "(Intercept) Beta0: tvalue=11.34 , pvalue=5.511950e-12\n",
    "\n",
    "(daily)     Beta1: tvalue=24.95, pvalue= 1.143068e-20\n",
    "\n",
    "As (pvalue=0)<(α=0.05); Reject Null hyp. Thus, X(YearsExperience) variable has good slope and variance w.r.t Y(Salary) variable.\n",
    "\n",
    "R-squared measures the strength of the relationship between your model and the dependent variable on a 0 – 100% scale.\n",
    "\n",
    "Measure goodness-of-fit by finding rsquared values (percentage of variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c23c18cc-c4c4-4001-aa40-ac34ac2a29f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9569566641435086, 0.9554194021486339)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.rsquared,model.rsquared_adj"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "873acf12-7fcf-4527-8b25-c9b966f23494",
   "metadata": {},
   "source": [
    "**Determination Coefficient = rsquared value = 0.95 ; very good fit >= 85%**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b63c7da6-6313-43c6-8c87-46200fad4f47",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# <font color='Green'>Step-8\n",
    "\n",
    "**Residual Analysis**\n",
    "\n",
    "**Test for Normality of Residuals (Q-Q Plot)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "623132d8-d03d-406c-ab54-2a9b6f706261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "sm.qqplot(model.resid, line = 'q')\n",
    "plt.title('Normal Q-Q plot of residuals of Model without any data transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcd09ba2-02a3-4f6a-aff8-1c1922c20f23",
   "metadata": {},
   "source": [
    "**As you can notice in the above plot,**\n",
    "+ The first model follows normal distribution\n",
    "\n",
    "**Residual Plot to check Homoscedasticity or Hetroscedasticity**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8ffde744-794e-46d0-98f0-ed2955df1bf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_standardized_values( vals ):\n",
    "    return (vals - vals.mean())/vals.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "842de2be-36a4-4181-b59d-6e49949981c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(get_standardized_values(model.fittedvalues), get_standardized_values(model.resid))\n",
    "plt.title('Residual Plot for Model without any data transformation')\n",
    "plt.xlabel('Standardized Fitted Values')\n",
    "plt.ylabel('Standardized Residual Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c412d9-e47d-4822-9e16-46033001b5b2",
   "metadata": {},
   "source": [
    "**As you can notice in the above plots,**\n",
    "+ The Model have Homoscedasciticity.\n",
    "+ The Residual(i.e Residual  = Actual Value - Predicted Value) and the Fitted values do not share any Pattern.\n",
    "+ Hence, there is no relation between the Residual and the Fitted Value. It is Randomly distributed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f97b69f-c2e9-4153-af75-bc3913003e91",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# <font color='Green'>Step-9\n",
    "\n",
    "**Model Validation**\n",
    "\n",
    "We will analyze Mean Squared Error (MSE) or Root Mean Squared Error (RMSE) — AKA the average distance (squared to get rid of negative numbers) between the model’s predicted target value and the actual target value.\n",
    "\n",
    "**Comparing different models with respect to the Root Mean Squared Errors**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b27a0451-82ba-4a76-8249-428e5a4aa372",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c667b08b-f33b-4a5f-8c7a-486c2af44277",
   "metadata": {},
   "outputs": [],
   "source": [
    "model1_pred_y =np.square(model1.predict(df['YearsExperience']))\n",
    "model2_pred_y =pow(model2.predict(df['YearsExperience']),3)\n",
    "model3_pred_y =np.exp(model3.predict(df['YearsExperience']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fdbd6828-4ec8-40f9-a4da-c0ea93a42b2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model= 5788.315051119395 \n",
      "model1= 5960.647096174309 \n",
      "model2= 6232.815455835842 \n",
      "model3= 7219.716974372806\n"
     ]
    }
   ],
   "source": [
    "model1_rmse =np.sqrt(mean_squared_error(df['Salary'], model1_pred_y))\n",
    "model2_rmse =np.sqrt(mean_squared_error(df['Salary'], model2_pred_y))\n",
    "model3_rmse =np.sqrt(mean_squared_error(df['Salary'], model3_pred_y))\n",
    "print('model=', np.sqrt(model.mse_resid),'\\n' 'model1=', model1_rmse,'\\n' 'model2=', model2_rmse,'\\n' 'model3=', model3_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "11e1155e-2126-498d-8664-c6cdf6482a67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'model'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = {'model': np.sqrt(model.mse_resid), 'model1': model1_rmse, 'model2': model3_rmse, 'model3' : model3_rmse}\n",
    "min(rmse, key=rmse.get)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac79be5f-bb5a-492e-bb0d-8c6bb0afcf15",
   "metadata": {},
   "source": [
    "**As model has the minimum RMSE and highest Adjusted R-squared score. Hence, we are going to use model to predict our values**\n",
    "+ Model is that Simple Linear regression model where we did not perfrom any data transformation and got the highest Adjusted R-squared value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19e79876-cde5-472a-81cd-2618c66e544a",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# <font color='Green'>Step-10\n",
    "\n",
    "Predicting Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5ed94bce-dbd3-4b58-ae7a-78f3cbc0cdcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Predicted_Salary_Hike</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "      <td>36187.158752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "      <td>38077.151217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "      <td>39967.143681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "      <td>44692.124842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "      <td>46582.117306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642.0</td>\n",
       "      <td>53197.090931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150.0</td>\n",
       "      <td>54142.087163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445.0</td>\n",
       "      <td>56032.079627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445.0</td>\n",
       "      <td>56032.079627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189.0</td>\n",
       "      <td>60757.060788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218.0</td>\n",
       "      <td>62647.053252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
       "      <td>63592.049484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
       "      <td>63592.049484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
       "      <td>57081.0</td>\n",
       "      <td>64537.045717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111.0</td>\n",
       "      <td>68317.030645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.9</td>\n",
       "      <td>67938.0</td>\n",
       "      <td>72097.015574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>66029.0</td>\n",
       "      <td>73987.008038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088.0</td>\n",
       "      <td>75877.000502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "      <td>81546.977895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</td>\n",
       "      <td>82491.974127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738.0</td>\n",
       "      <td>90051.943985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273.0</td>\n",
       "      <td>92886.932681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "      <td>101302.0</td>\n",
       "      <td>100446.902538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812.0</td>\n",
       "      <td>103281.891235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.7</td>\n",
       "      <td>109431.0</td>\n",
       "      <td>108006.872395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582.0</td>\n",
       "      <td>110841.861092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
       "      <td>115566.842252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
       "      <td>116511.838485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391.0</td>\n",
       "      <td>123126.812110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872.0</td>\n",
       "      <td>125016.804574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience    Salary  Predicted_Salary_Hike\n",
       "0               1.1   39343.0           36187.158752\n",
       "1               1.3   46205.0           38077.151217\n",
       "2               1.5   37731.0           39967.143681\n",
       "3               2.0   43525.0           44692.124842\n",
       "4               2.2   39891.0           46582.117306\n",
       "5               2.9   56642.0           53197.090931\n",
       "6               3.0   60150.0           54142.087163\n",
       "7               3.2   54445.0           56032.079627\n",
       "8               3.2   64445.0           56032.079627\n",
       "9               3.7   57189.0           60757.060788\n",
       "10              3.9   63218.0           62647.053252\n",
       "11              4.0   55794.0           63592.049484\n",
       "12              4.0   56957.0           63592.049484\n",
       "13              4.1   57081.0           64537.045717\n",
       "14              4.5   61111.0           68317.030645\n",
       "15              4.9   67938.0           72097.015574\n",
       "16              5.1   66029.0           73987.008038\n",
       "17              5.3   83088.0           75877.000502\n",
       "18              5.9   81363.0           81546.977895\n",
       "19              6.0   93940.0           82491.974127\n",
       "20              6.8   91738.0           90051.943985\n",
       "21              7.1   98273.0           92886.932681\n",
       "22              7.9  101302.0          100446.902538\n",
       "23              8.2  113812.0          103281.891235\n",
       "24              8.7  109431.0          108006.872395\n",
       "25              9.0  105582.0          110841.861092\n",
       "26              9.5  116969.0          115566.842252\n",
       "27              9.6  112635.0          116511.838485\n",
       "28             10.3  122391.0          123126.812110\n",
       "29             10.5  121872.0          125016.804574"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First model results without any transformation\n",
    "\n",
    "predicted2 = pd.DataFrame()\n",
    "predicted2['YearsExperience'] = df.YearsExperience\n",
    "predicted2['Salary'] = df.Salary\n",
    "predicted2['Predicted_Salary_Hike'] = pd.DataFrame(model.predict(predicted2.YearsExperience))\n",
    "predicted2"
   ]
  }
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